*2.3. Transmission Electron Microscopy (TEM)*

Transmission electron microscopy (TEM) and high resolution TEM (HRTEM) analysis were performed using a JEOL 3010 high resolution electron microscope (0.17 nm point-to-point resolution at Scherzer defocus), operating at 300 kV, equipped with a Gatan slow-scan CCD camera (model 794) and an Oxford Instrument EDS microanalysis detector Model 6636. Drops of colloidal solutions were deposited on holey-carbon copper grids and let dry at room temperature before analysis.


**Acknowledgements**

The author wishes to acknowledge the technical assistance of K. López, J.G. Morales, F. J. Jaimes, A. Morales and J.A. Lara‐Velázquez. The support of DGAPA‐PAPIIT project under contract IN100‐216 is also acknowledged.


Figure 8 shows the analysis of these measurements, using the technique PCA (Principal Component Analysis). The different measurement groups can be seen, clearly differentiated in samples of regular and export type coffee. The measurements taken from export quality coffee are highlighted in green circles, while the measurements taken from regular coffee are in red circles.

Fig. 8. Results of PCA analysis between measures of good quality coffee (green circles) and coffee with defects (red circles).

Fig. 9. Classification results of the measurements with a radial basis neural network, between good quality coffee (green circles) and coffee with defects (red circles).

Quality Control Through Electronic Nose System 515

Fig. 11. Validation Results of measurements with passion fruit (yellow circle), peaches (red circle) and apples (blue circle) using a neural network Feed Forward Back Propagation.

Electronic nose systems have been widely used for classification, discrimination of characteristics and detection of different elements or compounds considering the organoleptic characteristics, but its application in quantification tasks has not been widely explored. In some of these studies the least square regression method is used to consider the gas concentration (Khalaf et al., 2009) and for the quantification of mixed contaminants in the air (Zhou et al., 2006), also the new technologies have been used as systems based on micro-electromechanical sensors for the quantification of components in vapor mixtures

Below is a study with an electronic nose system, where a digital signal processor DSP was adapted and artificial neural network "Feed-forward back propagation" was implemented, which was trained with the aim of identifying and quantifying levels of Ethanol and Methanol in different samples. As result the percentage of Ethanol and Methanol of the samples were obtained, and the electronic nose system was improved, called "A-NOSE" (Rodriguez et al., 2010), when the processing software was implemented in a different

The artificial neural network that was used to perform the identification and quantification of Ethanol and Methanol was Multi Layer Perceptron (MLP) Feed-forward back propagation network, which was trained and tried in R2006a Matlab software; as a result of training of the artificial neuronal network the weight matrices and bias vectors were obtained, that were used to codify the artificial neural network program in C++ language with software CodeWarrior and subsequently downloaded this program in the digital signal

The initial samples were 95% Ethanol and 95% Methanol, which were diluted with distilled water to obtain 50%, 25% and 10% concentrations. Different measurements with the Ethanol and Methanol were realized in their different concentrations to realize the training of the

**3.3 Quality control of alcoholic beverages**

(Zhao et al, 2007).

device from the personal computer.

processor DSP56F801 of Motorola.

This group of measurements was classified using a radial basis neural network (Figure 9). It can be seen how the various measurements of the same type are located within a horizontal axis, forming 5 different subgroups, which belong to two major groups of export type coffee (green circles) and regular coffee (red circles).

#### **3.2 Quality control of fruits**

For the analysis of fruits invasive and noninvasive techniques can be used. Invasive techniques involve damaging the fruit to take a sample, in order to perform various tests with the same fruit at the same moment and also facilitate extraction of volatile particles, as manipulation helps to release more volatile particles, which facilitates the measurement process. A drawback of this technique is that once the product is handled it can only serve in the measurement process, because handling accelerates the decomposition process. Meanwhile, the noninvasive techniques, just take the fruit for testing without inflicting damage therefore the same fruit can be used for further testing in order to analyze maturity stages and study the processes of decomposition. (Rodriguez & Duran, 2008; Duran & Baldovino, 2009; Berna, 2010).

Figure 10 shows the results of the analysis of some measurements made on samples of passion fruit, peaches and apples, using the PCA technique. The 2 measurement groups can be seen, which can be clearly differentiated in samples of passion fruit and peaches, in addition 2 measurements of apple were introduced as a test (Creole apple and Chilean apple), in order to test the classification accuracy of the system and the similarity that may exist between different varieties of a fruit. Also Figure 11 shows the validation of the measurements using an Artificial Neural Network Feed Forward Back Propagation, applying the technique "Leave one out", it can be seen how the system responds to the eventual absence of a measure in the training of neural network, the most significant result occurs with measurements of apples which are classified successfully despite having so few measures.

Fig. 10. Results of PCA analysis between passion fruit (yellow circle), peaches (red circle) and apples (blue circle).

This group of measurements was classified using a radial basis neural network (Figure 9). It can be seen how the various measurements of the same type are located within a horizontal axis, forming 5 different subgroups, which belong to two major groups of export type coffee

For the analysis of fruits invasive and noninvasive techniques can be used. Invasive techniques involve damaging the fruit to take a sample, in order to perform various tests with the same fruit at the same moment and also facilitate extraction of volatile particles, as manipulation helps to release more volatile particles, which facilitates the measurement process. A drawback of this technique is that once the product is handled it can only serve in the measurement process, because handling accelerates the decomposition process. Meanwhile, the noninvasive techniques, just take the fruit for testing without inflicting damage therefore the same fruit can be used for further testing in order to analyze maturity stages and study the processes of decomposition. (Rodriguez & Duran, 2008; Duran &

Figure 10 shows the results of the analysis of some measurements made on samples of passion fruit, peaches and apples, using the PCA technique. The 2 measurement groups can be seen, which can be clearly differentiated in samples of passion fruit and peaches, in addition 2 measurements of apple were introduced as a test (Creole apple and Chilean apple), in order to test the classification accuracy of the system and the similarity that may exist between different varieties of a fruit. Also Figure 11 shows the validation of the measurements using an Artificial Neural Network Feed Forward Back Propagation, applying the technique "Leave one out", it can be seen how the system responds to the eventual absence of a measure in the training of neural network, the most significant result occurs with measurements of apples which are classified successfully despite having so few

Fig. 10. Results of PCA analysis between passion fruit (yellow circle), peaches (red circle)

(green circles) and regular coffee (red circles).

**3.2 Quality control of fruits**

Baldovino, 2009; Berna, 2010).

measures.

and apples (blue circle).

Fig. 11. Validation Results of measurements with passion fruit (yellow circle), peaches (red circle) and apples (blue circle) using a neural network Feed Forward Back Propagation.

#### **3.3 Quality control of alcoholic beverages**

Electronic nose systems have been widely used for classification, discrimination of characteristics and detection of different elements or compounds considering the organoleptic characteristics, but its application in quantification tasks has not been widely explored. In some of these studies the least square regression method is used to consider the gas concentration (Khalaf et al., 2009) and for the quantification of mixed contaminants in the air (Zhou et al., 2006), also the new technologies have been used as systems based on micro-electromechanical sensors for the quantification of components in vapor mixtures (Zhao et al, 2007).

Below is a study with an electronic nose system, where a digital signal processor DSP was adapted and artificial neural network "Feed-forward back propagation" was implemented, which was trained with the aim of identifying and quantifying levels of Ethanol and Methanol in different samples. As result the percentage of Ethanol and Methanol of the samples were obtained, and the electronic nose system was improved, called "A-NOSE" (Rodriguez et al., 2010), when the processing software was implemented in a different device from the personal computer.

The artificial neural network that was used to perform the identification and quantification of Ethanol and Methanol was Multi Layer Perceptron (MLP) Feed-forward back propagation network, which was trained and tried in R2006a Matlab software; as a result of training of the artificial neuronal network the weight matrices and bias vectors were obtained, that were used to codify the artificial neural network program in C++ language with software CodeWarrior and subsequently downloaded this program in the digital signal processor DSP56F801 of Motorola.

The initial samples were 95% Ethanol and 95% Methanol, which were diluted with distilled water to obtain 50%, 25% and 10% concentrations. Different measurements with the Ethanol and Methanol were realized in their different concentrations to realize the training of the

Quality Control Through Electronic Nose System 517

Fig. 13. Results of PCA analysis for the classification of wines and Aguardiente, according to

Fig. 14. Results of PCA analysis for the classification of wines and Aguardiente, according to

the concentration of Ethanol.

the concentration of Ethanol and Methanol.

artificial neuronal network and additionally other measures with wines (red, white, fruity, orange wine) and Aguardiente (national drink) were performed. The percentages of wine were close to 10% Ethanol and 0% Methanol and Aguardiente was close to 30% Ethanol and 0% Methanol, values in accordance to the values specified on the product labels.

Fig. 12. Results of PCA analysis between Ethanol measurements (blue circles) and Methanol measurements (red circles).

Figure 12 the results of PCA analysis can be seen applied to measurements of different ethanol and methanol concentrations. It can be inferred that the measurements follow a trend, which yield a characteristic equation that models the behavior for different concentrations of Ethanol and Methanol. It can also be seen that as the concentration of Ethanol and Methanol is lower measurements tend to find a common point, this may be because they have are both alcohol.

Another test analyzed samples of different kinds of wines (e.g.: red wine, white wine, orange wine) and Aguardiente (national drink), the results are shown in Figures 13 and 14. It can be seen that wine measurements are close to 10% Ethanol, the results obtained by the neural network (feed-forward back propagation) that was trained for this purpose showed results very close to 10% and 0% Ethanol Methanol. It should be clarified that the neural network was trained with data from measurements of different ethanol and methanol concentrations and were then tested with data from measurements of different drinks.

## **4. Other applications of electronic nose systems**

The applications of electronic nose systems are very diverse. The previous sections have covered some of the possible applications in the agro-food industry, but there are many other still to be mentioned, for example: The identification and diagnosis of respiratory

artificial neuronal network and additionally other measures with wines (red, white, fruity, orange wine) and Aguardiente (national drink) were performed. The percentages of wine were close to 10% Ethanol and 0% Methanol and Aguardiente was close to 30% Ethanol and

Fig. 12. Results of PCA analysis between Ethanol measurements (blue circles) and Methanol

Figure 12 the results of PCA analysis can be seen applied to measurements of different ethanol and methanol concentrations. It can be inferred that the measurements follow a trend, which yield a characteristic equation that models the behavior for different concentrations of Ethanol and Methanol. It can also be seen that as the concentration of Ethanol and Methanol is lower measurements tend to find a common point, this may be

Another test analyzed samples of different kinds of wines (e.g.: red wine, white wine, orange wine) and Aguardiente (national drink), the results are shown in Figures 13 and 14. It can be seen that wine measurements are close to 10% Ethanol, the results obtained by the neural network (feed-forward back propagation) that was trained for this purpose showed results very close to 10% and 0% Ethanol Methanol. It should be clarified that the neural network was trained with data from measurements of different ethanol and methanol concentrations and were then tested with data from measurements of different drinks.

The applications of electronic nose systems are very diverse. The previous sections have covered some of the possible applications in the agro-food industry, but there are many other still to be mentioned, for example: The identification and diagnosis of respiratory

measurements (red circles).

because they have are both alcohol.

**4. Other applications of electronic nose systems**

0% Methanol, values in accordance to the values specified on the product labels.

Fig. 13. Results of PCA analysis for the classification of wines and Aguardiente, according to the concentration of Ethanol.

Fig. 14. Results of PCA analysis for the classification of wines and Aguardiente, according to the concentration of Ethanol and Methanol.

Quality Control Through Electronic Nose System 519

flow control unit, a temperature control unit and a sorbent tube. The theoretical analysis and experimental results indicate that gas condensation, together with the medical electronic nose system can significantly reduce the detection limit of the nose system and increase the

Fig. 15. Results of PCA analysis for the classification of COPD patients and non-smokers.

Fig. 16. Results of PCA analysis with emphasis on measurements of patients with COPD.

Using DGN (Center), displaying the first 2 components.

system's ability to distinguish low concentration gas samples.

diseases (Xu et al., 2008, Velasquez et al ., 2009), the detection of narcotics and explosive substances (Oakes, L.; Dobrokhotov, V., 2010), determination of air quality and the environment (Zhou et al., 2006), among others, although there are still many possible applications to be explored.

## **4.1 Detection of diseases using electronic nose systems**

There are a variety of respiratory diseases, which in some cases are caused by smoking and exposure to contaminated environments. This is the case of Chronic Obstructive Pulmonary Disease COPD, which has a mortality rate exceeding 15.9% (Velásquez et al., 2009; Velásquez, 2008).

COPD is a chronic lung disease characterized by airflow limitation that is not fully reversible, with progressive deterioration and is associated with abnormal lung inflammatory response to noxious particles or gases (Velásquez et al., 2009; Velásquez, 2008). The main cause of COPD is prolonged consumption of cigarettes, it is said that up to 20% of smokers have COPD.

This disease is more common in:

## **4.1.1 Analysis of measurements taken from people with COPD and healthy controls**

Below are some results of the analysis of measurements taken from healthy controls, nonsmokers and patients diagnosed with COPD. All patients diagnosed with COPD were long time smokers from 16 years up to even 50 years and most of them have already quit smoking, due to the fact that many receive medical treatment. (Velásquez et al., 2009; Velásquez, 2008).

Figure 15 shows the results of PCA analysis of samples of healthy controls and patients with COPD. The low dispersion of measurements of healthy controls and high dispersion of measurements of patients with COPD can be seen; due the fact that to not all patients have the disease at the same level.

Figure 16 has separated the samples from different patients with COPD, from these results it can be inferred that according to the health of the person the different patients can be classified. Future research should conduct more measurements on patients at different stages of the disease and could also be extended to other respiratory diseases and even gastric related diseases.

A study that deserves attention is (Xu et al., 2008) who developed a solid trap/thermal desorption-based odorant gas condensation system designed and implemented for measuring low concentration odorant gas. The results showed that the technique was successfully applied to a medical electronic nose system. The developed system consists of a

diseases (Xu et al., 2008, Velasquez et al ., 2009), the detection of narcotics and explosive substances (Oakes, L.; Dobrokhotov, V., 2010), determination of air quality and the environment (Zhou et al., 2006), among others, although there are still many possible

There are a variety of respiratory diseases, which in some cases are caused by smoking and exposure to contaminated environments. This is the case of Chronic Obstructive Pulmonary Disease COPD, which has a mortality rate exceeding 15.9% (Velásquez et al., 2009;

COPD is a chronic lung disease characterized by airflow limitation that is not fully reversible, with progressive deterioration and is associated with abnormal lung inflammatory response to noxious particles or gases (Velásquez et al., 2009; Velásquez, 2008). The main cause of COPD is prolonged consumption of cigarettes, it is said that up to

**4.1.1 Analysis of measurements taken from people with COPD and healthy controls**  Below are some results of the analysis of measurements taken from healthy controls, nonsmokers and patients diagnosed with COPD. All patients diagnosed with COPD were long time smokers from 16 years up to even 50 years and most of them have already quit smoking, due to the fact that many receive medical treatment. (Velásquez et al., 2009;

Figure 15 shows the results of PCA analysis of samples of healthy controls and patients with COPD. The low dispersion of measurements of healthy controls and high dispersion of measurements of patients with COPD can be seen; due the fact that to not all patients have

Figure 16 has separated the samples from different patients with COPD, from these results it can be inferred that according to the health of the person the different patients can be classified. Future research should conduct more measurements on patients at different stages of the disease and could also be extended to other respiratory diseases and even

A study that deserves attention is (Xu et al., 2008) who developed a solid trap/thermal desorption-based odorant gas condensation system designed and implemented for measuring low concentration odorant gas. The results showed that the technique was successfully applied to a medical electronic nose system. The developed system consists of a

applications to be explored.

20% of smokers have COPD. This disease is more common in:

can damage the lungs.

Other COPD risk factors include:

Velásquez, 2008).

Velásquez, 2008).

the disease at the same level.

gastric related diseases.

**4.1 Detection of diseases using electronic nose systems**

flow control unit, a temperature control unit and a sorbent tube. The theoretical analysis and experimental results indicate that gas condensation, together with the medical electronic nose system can significantly reduce the detection limit of the nose system and increase the system's ability to distinguish low concentration gas samples.

Fig. 15. Results of PCA analysis for the classification of COPD patients and non-smokers.

Fig. 16. Results of PCA analysis with emphasis on measurements of patients with COPD. Using DGN (Center), displaying the first 2 components.

Quality Control Through Electronic Nose System 521

To identify measurement patterns or to carry out the training applications using computational intelligence it is very important to have expert staff on hand, as in the case of coffee quality control which had the support of trained coffee tasters, who issued their concept based on personal perception of each coffee sample, helping in the designation of the various patterns to facilitate subsequent classification tasks with different

Electronic nose systems have been widely used for classification, discrimination of characteristics and detection of different elements or compounds considering the organoleptic characteristics, and even for quantification tasks. This can be carried out with tools like multivariate analysis techniques and pattern recognition, such as PCA (Principal

The processing software of electronic nose system can be implemented on a digital signal processor DSP using an artificial neural network like the alcohol research case presented in section 3 which used a Feed-forward back propagation network, which was trained with the aim of identifying and quantifying Ethanol and Methanol of different samples. As result the

The artificial neural network that was used for the identification and quantification of Ethanol and Methanol was trained and tried using R2006a Matlab software; the training results were used to codify the the artificial neural network program in C++ language and subsequently downloaded this program in the digital signal processor DSP56F801 of

The results of PCA analysis for samples of healthy controls and patients with COPD showed differences in the low dispersion of the measurements taken of healthy controls and high dispersion of the measurements taken of patients with COPD; due to the fact that not all

This study was supported by the P09225 grant and carried out within the MIRP Research Group, Research Center, Instituto Tecnológico Metropolitano ITM, Medellín–Colombia.

Badrick, T. (2008). The Quality Control System. The Clinical Biochemist – Reviews,

Berna, A. (2010). Metal Oxide Sensors for Electronic Noses and Their Application to Food Analysis. Sensors, Vol.10, (April 2010), pp. 3882-3910, ISSN 1424-8220. [CENICAFE] Centro Nacional de Investigaciones del Café Colombia (March 2011). Sistemas

industrial Products Through of an Electronic Nose. Revista Colombiana de Tecnologías de Avanzada*,* Vol.1, No.13, (December 2009), pp. 1-8, ISSN 1692-7257. Duran, C. (2005). Diseño y optimización de los subsistemas de un sistema de olfato

electrónico para aplicaciones agroalimentarias e industriales. Universitat Rovira i

Based on Metal Oxide Semiconductor Sensors as an Alternative Technique for the

El Barbri, N.; Llobet, E.; El Bari, N.; Correig, X. & Bouchikhi, B. (2008). Electronic Nose

 http://cenicafe.org/modules.php?name=Sistemas\_Produccion&file=condclim Duran, C. & Baldovino, D. (2009). Monitoring System to Detect the Maturity of Agro-

Component Analysis) and ANN (Artificial Neural Networks).

percentage of Ethanol and Methanol of the samples were obtained.

(August,2008), p.p. 67-70, ISSN 0159 – 8090.

de Producción, 25.03.2011, Available from

Virgili. Tarragona, España.

patients have the disease at the same level.

**6. Acknowledgements**

**7. References**

measurements.

Motorola.

## **4.2 Determination of air quality using electronic nose systems**

Such applications have a bright future in the industry, because the environment is very susceptible to leakage and contamination by gases, which in many cases can be harmful and even lethal to humans.

NASA has done some work on this issue, for example (Ryan et al., 2009), Whom Developed an Electronic Nose to be used in Environmental Monitoring in the International Space Station, the Electronic Nose (Enose) is an array of 32 polymer sensors, the pattern of response may identify contaminants in the environment. An engineering test model of the ENose was used to monitor the air of the Early Human Test experiment at Johnson Space Center for 49 days. Examination of the data recorded by the ENose shows that major excursions in the resistance recorded in the sensor array may be correlated with events recorded in the Test Logs of the Test Chamber. The ability to monitor the constituents of breathing air in a closed chamber in which air is recycled is important to NASA for use in closed environments such as the space shuttle and the space station.

In the same way an electronic nose system could be developed for places such as airports or customs, in order to detect narcotics or prohibited hallucinogenic substances and in hostile or war environment to detect explosives or mines planted in the soil.

## **5. Conclusions**

The operation of the electronic nose system depends on the component parts and the features of the equipment. Inside we find the gas sensor array, the volatile particle delivery system, control system, data acquisition and data processing system.

We recommend a different chamber or hermetic compartment be used for containing the sample to be analyzed, called "Chamber of Concentration", provided the environmental and physical conditions of the system allow it.

The volatile particle transport system is fundamental because it affects the operation of the electronic nose system in the 3 different processes: concentration of volatile particles, measurement and cleanup.

Measurements with electronic nose systems begin by ensuring the adequacy of the sample to be examined, this depends on the type of element to be analyzed, which sometimes must be heated, cut, mixed with other elements or simply placed near the sensor array or in the chamber of concentration.

During the measurement process, the data acquisition system records all the changes in the output signal of each of the gas sensors. When the measurement process is finished the cleaning of the chamber of sensors begins, which is very important to restore the initial conditions of the system and to ensure the reproducibility of the measurements.

Once the measurement process is finished the stored data is processed and analyzed using the pre-processing software which allows to extraction of static parameters from the measurements and reduces the amount of information to be analyzed. Subsequently the processing software is applied, in order to obtain an olfactory footprint that represents the sample, to perform classification, discrimination and other tasks.

Coffee is preferably analyzed in the same way as by tasters, who perform tests on toasted and ground coffee, therefore the coffee must be roasted and ground in order to obtain a powder which is mixed with hot water, to facilitate the emission of volatile particles and this mixture is introduced into the chamber of concentration for the measurement process. This procedure for the preparation of the mixture can be applied similarly to other elements before the start of the measurements.

Such applications have a bright future in the industry, because the environment is very susceptible to leakage and contamination by gases, which in many cases can be harmful and

NASA has done some work on this issue, for example (Ryan et al., 2009), Whom Developed an Electronic Nose to be used in Environmental Monitoring in the International Space Station, the Electronic Nose (Enose) is an array of 32 polymer sensors, the pattern of response may identify contaminants in the environment. An engineering test model of the ENose was used to monitor the air of the Early Human Test experiment at Johnson Space Center for 49 days. Examination of the data recorded by the ENose shows that major excursions in the resistance recorded in the sensor array may be correlated with events recorded in the Test Logs of the Test Chamber. The ability to monitor the constituents of breathing air in a closed chamber in which air is recycled is important to NASA for use in

In the same way an electronic nose system could be developed for places such as airports or customs, in order to detect narcotics or prohibited hallucinogenic substances and in hostile

The operation of the electronic nose system depends on the component parts and the features of the equipment. Inside we find the gas sensor array, the volatile particle delivery

We recommend a different chamber or hermetic compartment be used for containing the sample to be analyzed, called "Chamber of Concentration", provided the environmental and

The volatile particle transport system is fundamental because it affects the operation of the electronic nose system in the 3 different processes: concentration of volatile particles,

Measurements with electronic nose systems begin by ensuring the adequacy of the sample to be examined, this depends on the type of element to be analyzed, which sometimes must be heated, cut, mixed with other elements or simply placed near the sensor array or in the

During the measurement process, the data acquisition system records all the changes in the output signal of each of the gas sensors. When the measurement process is finished the cleaning of the chamber of sensors begins, which is very important to restore the initial

Once the measurement process is finished the stored data is processed and analyzed using the pre-processing software which allows to extraction of static parameters from the measurements and reduces the amount of information to be analyzed. Subsequently the processing software is applied, in order to obtain an olfactory footprint that represents the

Coffee is preferably analyzed in the same way as by tasters, who perform tests on toasted and ground coffee, therefore the coffee must be roasted and ground in order to obtain a powder which is mixed with hot water, to facilitate the emission of volatile particles and this mixture is introduced into the chamber of concentration for the measurement process. This procedure for the preparation of the mixture can be applied similarly to other elements

conditions of the system and to ensure the reproducibility of the measurements.

sample, to perform classification, discrimination and other tasks.

**4.2 Determination of air quality using electronic nose systems**

closed environments such as the space shuttle and the space station.

or war environment to detect explosives or mines planted in the soil.

system, control system, data acquisition and data processing system.

physical conditions of the system allow it.

measurement and cleanup.

chamber of concentration.

before the start of the measurements.

even lethal to humans.

**5. Conclusions**

To identify measurement patterns or to carry out the training applications using computational intelligence it is very important to have expert staff on hand, as in the case of coffee quality control which had the support of trained coffee tasters, who issued their concept based on personal perception of each coffee sample, helping in the designation of the various patterns to facilitate subsequent classification tasks with different measurements.

Electronic nose systems have been widely used for classification, discrimination of characteristics and detection of different elements or compounds considering the organoleptic characteristics, and even for quantification tasks. This can be carried out with tools like multivariate analysis techniques and pattern recognition, such as PCA (Principal Component Analysis) and ANN (Artificial Neural Networks).

The processing software of electronic nose system can be implemented on a digital signal processor DSP using an artificial neural network like the alcohol research case presented in section 3 which used a Feed-forward back propagation network, which was trained with the aim of identifying and quantifying Ethanol and Methanol of different samples. As result the percentage of Ethanol and Methanol of the samples were obtained.

The artificial neural network that was used for the identification and quantification of Ethanol and Methanol was trained and tried using R2006a Matlab software; the training results were used to codify the the artificial neural network program in C++ language and subsequently downloaded this program in the digital signal processor DSP56F801 of Motorola.

The results of PCA analysis for samples of healthy controls and patients with COPD showed differences in the low dispersion of the measurements taken of healthy controls and high dispersion of the measurements taken of patients with COPD; due to the fact that not all patients have the disease at the same level.

## **6. Acknowledgements**

This study was supported by the P09225 grant and carried out within the MIRP Research Group, Research Center, Instituto Tecnológico Metropolitano ITM, Medellín–Colombia.

## **7. References**

http://cenicafe.org/modules.php?name=Sistemas\_Produccion&file=condclim

**28**

*France*

**Mammographic Quality Control Using Digital**

Breast cancer is the leading cause of cancer mortality among middle aged women. Survival and recovery depend on early diagnosis. At present mammography is one of the most reliable methods for early breast cancer detection. However relevance of diagnosis is highly correlated to image quality of the mammographic system. Hence periodic controls in mammographic facilities are necessary in order to make sure they work properly. In particular global image quality is evaluated from a mammographic phantom film. A phantom is an object with the same anatomic shape and radiological response as an average dense fleshed breast and in which are embedded structures that mimic clinically relevant features such as microcalcifications, nodules and fibrils. For each category of features, the targets have progressively smaller sizes and contrast so that the largest one is the most readily visible and the next is less visible and so on. Using a phantom makes it possible to free from the variable of differences in breast tissue positioning and level of compression from patient to patient. The process is as follows: the mammographic phantom film is analysed independently by several readers and a score is obtained by each of them depending on the number of objects they see. The independent object visibility scores are

Automating this score by using computer image processing of digitized phantom films should make the evaluation of mammographic facilities easier and less subjective. In addition image processing should enable us to take into account other parameters such as, for instance, noise, texture and shape of the targets that a reader eye cannot estimate quantitatively, and so to perform a more elaborate analysis. In collaboration with ARCADES (Association pour la Recherche et le Dépistage des Cancers du Sein et du col de l'utérus) which set, since 1989, a breast cancer screening program in South of France, a project aimed at automating phantom film evaluation is in progress. Such a project consists first in digitizing phantom films with the adequate spatial resolution and then in processing the obtained images in order to detect,

Little work has been done to automate quality control in mammographic facilities. Fast Fourier transform is used (Brooks et al., 1997) to establish some visibility criteria for the phantom test object. (Chakraborty et al., 1997) compares phantom images with a pattern image to obtain relations between some of the image parameters and the physical conditions

then averaged and the resulting score is assigned to the phantom film.

segment and characterize the objects contained in the phantom.

**1. Introduction**

**Image Processing Techniques**

*Université Paul Cézanne, Institut Fresnel, UMR-CNRS 6133*

Mouloud Adel and Monique Rasigni

*Domaine Universitaire de Saint Jérôme,*

Spoilage Classification of Red Meat. Sensors, Vol.8, (January 2008), pp. 142-156, ISSN 1424-8220.

## **Mammographic Quality Control Using Digital Image Processing Techniques**

Mouloud Adel and Monique Rasigni *Université Paul Cézanne, Institut Fresnel, UMR-CNRS 6133 Domaine Universitaire de Saint Jérôme, France*

### **1. Introduction**

522 Modern Approaches To Quality Control

Falasconi, M.; Pardo, M.; Sberveglieri, G.; Ricco, I. & Bresciani, A. (2005). The novel EOS835

Grupo E-Nose, (March 2011) ¿Qué es una Nariz Electrónica?, 15.04.2011, Available from

Khalaf, W.; Pace, C. & Gaudioso, M. (2009). Least Square Regression Method for estimating

Oakes, L.; Dobrokhotov, V. (2010). Electronic Nose for Detection of Explosives. American

Pardo, M.; Niederjaufner, G.; Benussi, G.; Comini, E.; Faglia, G.; Sberveglieri, G.; Holmberg,

Institute of Technology. Johnson Space Center, NASA, Houston TX 77058. Rodríguez, J.; Durán, C.; Reyes, A. (2010). Electronic Nose for Quality Control of

Rodriguez, J. & Duran, C. (2008). Electronic odor system to detect volatile compounds.

Tian, F.; Yang, S. & Dong, K. (2005). Circuit and Noise Analysis of Odorant Gas Sensors in an E-Nose. Sensors, Vol.5, (February 2005), pp. 85-96, ISSN 1424-8220. Velásquez, A.; Durán, C.; Gualdron, O.; Rodríguez, J. & Manjarres, L. (2009). Electronic

Velásquez, A. (2008). Sistema Multisensorial Electrónico No Invasivo Para La Detección De

Wilson, A. & Baietto, M. (2009). Applications and Advances in Electronic-Nose Technologies. Sensors, Vol.9, (June 2009), pp. 5099-5148, ISSN 1424-8220. Xu, X.; Tian, F.; Yang, S.; Jia, Q. & Ma, J. (2008). A Solid Trap and Thermal Desorption

Zhao, W.; Pinnaduwagel, L.; Leis, J.W.; Gehl, A.C.; Allman, S.L.; Shepp,A. & Mahmud, K.K.

based electronic nose. Applied Physics Letters, 91 (4). ISSN 0003-6951. Zhou, H.; Homer, M.; Shevade, A. & Ryan, M. (2006). Nonlinear Least-Squares Based

http://www.e-nose.com.ar/paginas/funcionamiento.htm

ISSN 1424-8220.

ISSN 1424-8220.

1692-7257.

April 15-17, 2009.

Santander, Colombia.

2008), pp. 6885-6898, ISSN 1424-8220.

Physical Society. (March 2010).

(December 2009), pp. 36-46, ISSN 1424-8220.

0925-4005.

Spoilage Classification of Red Meat. Sensors, Vol.8, (January 2008), pp. 142-156,

electronic nose and data analysis for evaluating coffee ripening, Sensors and Actuators B: Chemical, Volume 110, Issue 1, (September 2005), p.p 73-80, ISSN

gas concentration in an Electronic Nose System. Sensors, Vol. 9, pp. 1678-1691,

M. & Lundstrom, I. (2000). Data preprocessing enhances the classification of different brands of Espresso coffee with an electronic nose, Sensors and Actuators B: Chemical, Volume 69, Issue 3, (October 2000), p.p 397-403, ISSN 0925-4005. Ryan, M.; Homer, M.; Buehler, M.; Manatt, K. & Zee, F. (2009). Monitoring the Air Quality in a

Closed Chamber Using an Electronic Nose. Jet Propulsion Laboratory, California

Colombian Coffee through the Detection of Defects in "Cup Tests". Sensors, Vol.10,

Revista Colombiana de Tecnologías de Avanzada*,* Vol.2, No.12, pp. 20-26, ISSN

Nose to Detect Patients with COPD From Exhaled Breath. Proceedings of the 13th International Symposium on Olfaction and Electronic Nose. AIP Conference Proceedings, Volume 1137, pp. 452-454, ISBN: 978-0-7354-0674-2, Brescia, Italy,

La Patología Respiratoria Epoc. Universidad de Pamplona. Pamplona, Norte de

System with Application to a Medical Electronic Nose. Sensors, Vol.8, (November

(2007) Quantitative analysis of ternary vapor mixtures using a microcantilever-

Method for Identifying and Quantifying Single and Mixed Contaminants in Air with an Electronic Nose. Sensors, Vol.6, (December 2005), pp. 1-18, ISSN 1424-8220.

Breast cancer is the leading cause of cancer mortality among middle aged women. Survival and recovery depend on early diagnosis. At present mammography is one of the most reliable methods for early breast cancer detection. However relevance of diagnosis is highly correlated to image quality of the mammographic system. Hence periodic controls in mammographic facilities are necessary in order to make sure they work properly. In particular global image quality is evaluated from a mammographic phantom film. A phantom is an object with the same anatomic shape and radiological response as an average dense fleshed breast and in which are embedded structures that mimic clinically relevant features such as microcalcifications, nodules and fibrils. For each category of features, the targets have progressively smaller sizes and contrast so that the largest one is the most readily visible and the next is less visible and so on. Using a phantom makes it possible to free from the variable of differences in breast tissue positioning and level of compression from patient to patient. The process is as follows: the mammographic phantom film is analysed independently by several readers and a score is obtained by each of them depending on the number of objects they see. The independent object visibility scores are then averaged and the resulting score is assigned to the phantom film.

Automating this score by using computer image processing of digitized phantom films should make the evaluation of mammographic facilities easier and less subjective. In addition image processing should enable us to take into account other parameters such as, for instance, noise, texture and shape of the targets that a reader eye cannot estimate quantitatively, and so to perform a more elaborate analysis. In collaboration with ARCADES (Association pour la Recherche et le Dépistage des Cancers du Sein et du col de l'utérus) which set, since 1989, a breast cancer screening program in South of France, a project aimed at automating phantom film evaluation is in progress. Such a project consists first in digitizing phantom films with the adequate spatial resolution and then in processing the obtained images in order to detect, segment and characterize the objects contained in the phantom.

Little work has been done to automate quality control in mammographic facilities. Fast Fourier transform is used (Brooks et al., 1997) to establish some visibility criteria for the phantom test object. (Chakraborty et al., 1997) compares phantom images with a pattern image to obtain relations between some of the image parameters and the physical conditions

Mammographic Quality Control Using Digital Image Processing Techniques 525

*M1*

relative size of features.

(b) : mass; (c) : fibre

**3.1 General description**

last image segmentation is done.

*M5*

*M7*

*Basis*

*M6 N2*

*N1*

**3. Image processing of digitized phantom films**

**3.2 Local contrast modification method description**

*N5*

*B B D DD*

Fig. 1. Schematic diagram of the phantom MTM 100/R showing the locations and the

Fig. 2. Subimages extracted from a digitized phantom film. (a) : group of microcalcifications

Two pre-processing steps are applied to each extracted subimage before the segmentation step as shown in Fig. 3. Because of the noisy nature of these subimages, a noise reduction method is used as a first processing step. A contrast enhancement step is then applied. At

In classical image processing techniques, a fixed shape and a fixed size window around each pixel is used in order to convolve it with a defined filter. In order to take the local features

*N6*

*F1*

*F2*

*F4 F3*

*Envelope*

*F5*

*F6*

*F7*

*V*

*N7*

*C1 C2*

*H*

*Z*

*N3*

*N4*

*M2*

*M3*

*M4*

in which the images have been obtained. His work concerned only microcalcifications. (Dougherty, 1998) studies the most prominent microcalcification group and the most prominent mass using a manual threshold and some mathematical morphology operators. (Castellano et al., 1998) used binary masks to locate microcalcifications and they studied image resolution scales contained in the phantom. (Blot et al., 2003) used grey level cooccurrence matrices to score structures embedded in the phantom. (Mayo et al., 2004) used region growing and morphological operators to segment and characterize microcalcifications. They also studied horizontal resolution areas using morphological operators. This chapter presents a feasibility study aiming at automating phantom scoring using image processing techniques on digitized phantom films.

In the following sections, we describe the phantom used, the mammographic phantom image acquisition and digitization, the noise reduction and the contrast enhancement schemes used for processing phantom images, the segmentation step for each object (microcalcifications, masses and fibres) and the results obtained on nine phantoms films.

## **2. Description, acquisition and digitization of phantom films**

#### **2.1 Phantom description**

The phantom used in this study is the MTM 100/R (Computerized Imaging Reference Systems, Inc., 2428 Almeda Avenue Suite 212, Norfolk, VA 23513, U.S.A., Phantom Serial Number: 2788). The MTM 100/R is used in France for Mammography Accreditation. It is made of tissue equivalent material in which are embedded objects simulating 7 pentagonalshaped groups of microcalcifications (M1 to M7), 7 masses (N1 to N7) and 7 fibres (F1 to F7). For each category of features, the targets have progressively smaller size and contrast so that the largest one is the most readily visible, the next is less visible and so on. For convenience M1 stands for the microcalcification group containing the largest specks and M7, the smallest ones, with a similar convention for the other target structures. Inside are also present vertical (V) and horizontal (H) spatial resolution scales (line pair target : 20 lp/mm), a delimited zone (Z) for measuring a reference optical density, two different optical density contiguous areas (C1 and C2) for defining contrast, three cavities (D) for x-ray dose measurement and at last small balls (B) for x-ray alignment control. Figure 1 shows a schematic diagram of the MTM 100/R phantom.

## **2.2 Acquisition and digitization of phantom films**

Phantom films are digitized with an ultra high resolution drum scanner (Scanmate 11000- ScanView A/S.Meterbuen 6. DK-2740 Skovlunde. Denmark) which may digitize from 50 to 11000 dpi (dots per inch) and code images on 256 (8bits /pixel) or 16384 (14bits /pixel) grey levels. Spatial scanning resolution was chosen so that it approximately corresponds to the resolving power of a viewer (with a standard resolving power ~ 4.10-3rd) using a twice magnifying lens (such lenses are often used by radiologists for reading clinical mammograms). So phantom films were digitized with a resolution of 50 μm per pixel (or 508 dpi) and were coded on 256 grey levels.

For each category of structures (microcalcifications, masses and fibers) a sub-image was extracted from the digitized phantom image so that each sub-image contained one target and was roughly centered on it. Subimages sizes were 256256-pixels for microcalcification groups and masses and 412412-pixels for fibres. Fig. 2 shows an example of subimages extracted from a digitized phantom film.

in which the images have been obtained. His work concerned only microcalcifications. (Dougherty, 1998) studies the most prominent microcalcification group and the most prominent mass using a manual threshold and some mathematical morphology operators. (Castellano et al., 1998) used binary masks to locate microcalcifications and they studied image resolution scales contained in the phantom. (Blot et al., 2003) used grey level cooccurrence matrices to score structures embedded in the phantom. (Mayo et al., 2004) used region growing and morphological operators to segment and characterize microcalcifications. They also studied horizontal resolution areas using morphological operators. This chapter presents a feasibility study aiming at automating phantom scoring

In the following sections, we describe the phantom used, the mammographic phantom image acquisition and digitization, the noise reduction and the contrast enhancement schemes used for processing phantom images, the segmentation step for each object (microcalcifications, masses and fibres) and the results obtained on nine phantoms films.

The phantom used in this study is the MTM 100/R (Computerized Imaging Reference Systems, Inc., 2428 Almeda Avenue Suite 212, Norfolk, VA 23513, U.S.A., Phantom Serial Number: 2788). The MTM 100/R is used in France for Mammography Accreditation. It is made of tissue equivalent material in which are embedded objects simulating 7 pentagonalshaped groups of microcalcifications (M1 to M7), 7 masses (N1 to N7) and 7 fibres (F1 to F7). For each category of features, the targets have progressively smaller size and contrast so that the largest one is the most readily visible, the next is less visible and so on. For convenience M1 stands for the microcalcification group containing the largest specks and M7, the smallest ones, with a similar convention for the other target structures. Inside are also present vertical (V) and horizontal (H) spatial resolution scales (line pair target : 20 lp/mm), a delimited zone (Z) for measuring a reference optical density, two different optical density contiguous areas (C1 and C2) for defining contrast, three cavities (D) for x-ray dose measurement and at last small balls (B) for x-ray alignment control. Figure 1 shows a

Phantom films are digitized with an ultra high resolution drum scanner (Scanmate 11000- ScanView A/S.Meterbuen 6. DK-2740 Skovlunde. Denmark) which may digitize from 50 to 11000 dpi (dots per inch) and code images on 256 (8bits /pixel) or 16384 (14bits /pixel) grey levels. Spatial scanning resolution was chosen so that it approximately corresponds to the resolving power of a viewer (with a standard resolving power ~ 4.10-3rd) using a twice magnifying lens (such lenses are often used by radiologists for reading clinical mammograms). So phantom films were digitized with a resolution of 50 μm per pixel (or

For each category of structures (microcalcifications, masses and fibers) a sub-image was extracted from the digitized phantom image so that each sub-image contained one target and was roughly centered on it. Subimages sizes were 256256-pixels for microcalcification groups and masses and 412412-pixels for fibres. Fig. 2 shows an example of subimages

using image processing techniques on digitized phantom films.

**2.1 Phantom description**

schematic diagram of the MTM 100/R phantom.

508 dpi) and were coded on 256 grey levels.

extracted from a digitized phantom film.

**2.2 Acquisition and digitization of phantom films**

**2. Description, acquisition and digitization of phantom films**

Fig. 1. Schematic diagram of the phantom MTM 100/R showing the locations and the relative size of features.

Fig. 2. Subimages extracted from a digitized phantom film. (a) : group of microcalcifications (b) : mass; (c) : fibre

## **3. Image processing of digitized phantom films**

## **3.1 General description**

Two pre-processing steps are applied to each extracted subimage before the segmentation step as shown in Fig. 3. Because of the noisy nature of these subimages, a noise reduction method is used as a first processing step. A contrast enhancement step is then applied. At last image segmentation is done.

## **3.2 Local contrast modification method description**

In classical image processing techniques, a fixed shape and a fixed size window around each pixel is used in order to convolve it with a defined filter. In order to take the local features

Mammographic Quality Control Using Digital Image Processing Techniques 527

terms of grey levels is no longer satisfactory. Let c0 be the upper c value beyond which the percentage P0 is greater than 60%. The pixel (i,j) is assigned the window W=(c0+2)(c0+2). In the window W such as WWmax we finally define the "center" as the set of pixels having the mask value "1", and the "background" as the set of pixels having both the mask value "0" and which are 8-neighbourhood connected at least to a pixel "1". Pixels "0" which do not verify the previous constraint belong neither to the "center" nor to the "background" and are not taken into account later on. Fig. 4 gives an example the way the "center" and the

1 1 0 0 0 0 0

1 1 1 1 0 1 1

1 1 1 1 0 0 1 1 1 0 0

(a) (b) (c)

1 0 0 1 1 1 0

0 0 0 1 1 1 0

0 0 1

1 1 0 0

0

0

0 0

0 0

0 0

0 0

1 0

0 0

1 1 0

1 0 0

1 1 0

Fig. 4. Adaptive neighbourhood selection with a threshold value T=5; (a) Wmax window around bold-faced pixel value 15, Nmax=4; (b) Mask values associated to test pattern in (a); (c) The "center" (light grey)and the "background" (dark grey) areas around the bold-faced

After determining the "center" and the "background" regions around each pixel (i,j), a local

(, ) (, ) (, ) max ( , ), ( , ) *c b c b*

where Mc(i,j) and Mb(i,j) are the mean values, in image I, of pixels labelled as the "center" and as the "background" regions around pixel (i,j) respectively. Note that C(i,j) is within the

The local contrast image C obtained in the previous step is transformed into a new image C' such as C'(i,j)=(C(i,j)), where is a contrast modification function depending on features to

In image C' each pixel value is a contrast value. In order to obtain the corresponding image in the grey level domain, an inverse contrast transform of the process used to obtain image

*E i* , ( , )(1 '( , )) *<sup>b</sup> j M i j C i j* if Mb(i,j) Mc(i,j)

be detected. This function meets some requirements in the interval [0,1] :

*M ij M ij Ci j <sup>M</sup> ij M ij*

(1)

pixel. White cells correspond to pixels belonging neither to the "center" nor to the

1 1 1 1 1 0 1 0

1 1 1 1 0 1 1 1

1 1 1 1 1 0 0 0

1 1 1 1 0 0 1 1

"background" areas are determined.

9 8 13

13 13 10 8

7 7

5

6

9 8

8 8

8 8

8 6

16 9

16 17 10

17 16 7

15 16 10

contrast image C is computed from :

C from I (Eq. 1) is used as follows:

"background".

1 1 1 1 13 10 15 10

1 1 1 1 8 5 15 14

1 1 1 1 11 15 14 15

1 1 1 1 13 21 22 21

13 14 10 9 8 10 11

12 15 14 14 9 14 15

1 1 1 1 10 9 15 14 15 <sup>16</sup> <sup>4</sup>

13 20 19 14 16 15 10

7 6 20 15 **1** 16 10

range [0,1].

around each pixel into account, a variable shape and size neighbourhood is defined (Dhawan et al., 1986; Dhawan & Le Royer, 1988) using local statistics.

Noise reduction step consist in filtering two kinds of noise. The non uniform background considered as a ''low frequency noise'' and the radiographic noise (high frequency noise): film granularity and quantum mottle. Shadow correction of the background is adapted to each object (microcalcifications, masse and fibre), whereas the radiographic noise filter and the contrast enhancement steps are based on the same method, described in the next section. This method consists in computing a local contrast around each pixel using a variable neighbourhood whose size and shape depend on the statistical properties around the given pixel. The obtained image is then transformed into a new contrast image using various contrast modification functions. At last an inverse contrast transform is applied on the new contrast image to yield an enhanced version of the original image. Contrast enhancement step consists in enhancing image features while preserving details for the segmentation step. Image Segmentation is adapted to the objects to be detected and is presented in the following sections.

Fig. 3. Flowchart of the image processing steps applied to phantom images.

Each pixel (i,j) is assigned an upper window Wmax centered on it, whose size is (2Nmax+1)(2Nmax+1). We also define an inner area around the pixel (i,j) whose size is (cc) and an external area whose size is (c+2) (c+2), where c is an odd number. Let I(i,j) be the grey level of pixel (i,j) in image I, and T a given threshold. Pixel (k,l) within Wmax is assigned a binary mask value "0" if |I(k,l) –I(i,j)| > T, else it is assigned a binary mask value "1". Then the percentage P0 of zeros is computed over the region between the external (c+2)(c+2) and the inner (cc) areas, for each c in the range [1, 3, 5, … ,2Nmax – 1]. The process stops if this percentage is greater than 60% or if the upper window Wmax is reached. The value of 60% has been chosen because beyond this limit, we may consider too many pixels "0" are surrounding the inner area and so the notion of neighbourhood with the central pixel (i,j) in

around each pixel into account, a variable shape and size neighbourhood is defined

Noise reduction step consist in filtering two kinds of noise. The non uniform background considered as a ''low frequency noise'' and the radiographic noise (high frequency noise): film granularity and quantum mottle. Shadow correction of the background is adapted to each object (microcalcifications, masse and fibre), whereas the radiographic noise filter and the contrast enhancement steps are based on the same method, described in the next section. This method consists in computing a local contrast around each pixel using a variable neighbourhood whose size and shape depend on the statistical properties around the given pixel. The obtained image is then transformed into a new contrast image using various contrast modification functions. At last an inverse contrast transform is applied on the new contrast image to yield an enhanced version of the original image. Contrast enhancement step consists in enhancing image features while preserving details for the segmentation step. Image Segmentation is adapted to the objects to be detected and is presented in the

> Extracted subimage

> Noise reduction

Contrast enhancement

(Dhawan et al., 1986; Dhawan & Le Royer, 1988) using local statistics.

Fig. 3. Flowchart of the image processing steps applied to phantom images.

Each pixel (i,j) is assigned an upper window Wmax centered on it, whose size is (2Nmax+1)(2Nmax+1). We also define an inner area around the pixel (i,j) whose size is (cc) and an external area whose size is (c+2) (c+2), where c is an odd number. Let I(i,j) be the grey level of pixel (i,j) in image I, and T a given threshold. Pixel (k,l) within Wmax is assigned a binary mask value "0" if |I(k,l) –I(i,j)| > T, else it is assigned a binary mask value "1". Then the percentage P0 of zeros is computed over the region between the external (c+2)(c+2) and the inner (cc) areas, for each c in the range [1, 3, 5, … ,2Nmax – 1]. The process stops if this percentage is greater than 60% or if the upper window Wmax is reached. The value of 60% has been chosen because beyond this limit, we may consider too many pixels "0" are surrounding the inner area and so the notion of neighbourhood with the central pixel (i,j) in

Image Segmentation

following sections.

terms of grey levels is no longer satisfactory. Let c0 be the upper c value beyond which the percentage P0 is greater than 60%. The pixel (i,j) is assigned the window W=(c0+2)(c0+2). In the window W such as WWmax we finally define the "center" as the set of pixels having the mask value "1", and the "background" as the set of pixels having both the mask value "0" and which are 8-neighbourhood connected at least to a pixel "1". Pixels "0" which do not verify the previous constraint belong neither to the "center" nor to the "background" and are not taken into account later on. Fig. 4 gives an example the way the "center" and the "background" areas are determined.

Fig. 4. Adaptive neighbourhood selection with a threshold value T=5; (a) Wmax window around bold-faced pixel value 15, Nmax=4; (b) Mask values associated to test pattern in (a); (c) The "center" (light grey)and the "background" (dark grey) areas around the bold-faced pixel. White cells correspond to pixels belonging neither to the "center" nor to the "background".

After determining the "center" and the "background" regions around each pixel (i,j), a local contrast image C is computed from :

$$\mathbf{C}(i,j) = \frac{\left| M\_c(i,j) - M\_b(i,j) \right|}{\max\left( M\_c(i,j), M\_b(i,j) \right)} \tag{1}$$

where Mc(i,j) and Mb(i,j) are the mean values, in image I, of pixels labelled as the "center" and as the "background" regions around pixel (i,j) respectively. Note that C(i,j) is within the range [0,1].

The local contrast image C obtained in the previous step is transformed into a new image C' such as C'(i,j)=(C(i,j)), where is a contrast modification function depending on features to be detected. This function meets some requirements in the interval [0,1] :

$$-\,\psi(0) = 0 \text{ and } \psi(1) = 1.$$

In image C' each pixel value is a contrast value. In order to obtain the corresponding image in the grey level domain, an inverse contrast transform of the process used to obtain image C from I (Eq. 1) is used as follows:

$$E(i,j) = M\_b(i,j)(1 - C'(i,j)) \text{ if } M\_b(i,j) \ge M\_c(i,j)$$

Mammographic Quality Control Using Digital Image Processing Techniques 529

(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

Before applying noise reduction and contrast enhancement steps, inhomogeneous background of subimages containing microcalcification is extracted using the classical multiresolution Burt decomposition into level 3 (level 0 is the original image). A linear interpolation is then applied to obtain background image. The same method was not suitable to correct background for subimages containing masses and fibres, due to the object to image size ratio. Using the local contrast modification method described above with a big window size for Wmax, enabled us to obtain background image. At last corrected image for the whole objects is obtained by subtracting the background image from the original image

Applying the noise reduction and contrast enhancement steps described in the previous

Fig. 5. Correction of Background inhomogeneity on extracted subimages. (a), (d) and (g) : original extracted subimages; (b), (e) and (h) background images; (c), (f) and (i) corrected

images.

as shown in Fig. 5.

**3.2.2 Preprocessing of real phantom images**

section, yield the images shown in Fig. 6.

$$E(i,j) = \frac{M\_b(i,j)}{(1 - C'(i,j))}\quad\text{if }M\_b(i,j) \le M\_c(i,j)$$

This transform gives a new image E which is an enhanced version of image I. It is then possible to evaluate the efficiency of the method from comparison between images E and I.

#### **3.2.1 Performance evaluation on simulated images**

Several functions including square root, exponential, polynomial and trigonometric were tested (Guis et al., 2003). Actually functions which are over the line y=x increase the contrast but enhance the noise too. In the other hand, functions which are under the line y=x yield noise reduction. Because of the noisy nature of real images of phantom, the second kind of functions was chosen for enhancing the objects contained in these images. To choose suitable function , computer simulated images containing objects similar to those observed in the phantom film were generated with various contrast and noise levels. The aim of this simulation was to perform a quantitative evaluation of the noise reduction method described in previous sections. For each target, 6 noise-free images were generated each of them with a different contrast level. Three noise levels were then assigned to each contrast level image. Contrast level is defined as the difference between the mean grey level of the object and the mean grey level of the background divided by the mean grey level of the background. According to studies on radiographic noise, two types of noise sources, namely film granularity and quantum mottle, are present in an X-ray image. Spatially correlated Poisson noise model has to be considered in the case of mammographic films. In our simulations, a signal-dependent spatially uncorrelated Gaussian noise is used as a firstorder approximation of the Poisson noise model (Quian et al., 1994; Aghadasi et al., 1992; Kuan et al., 1985) namely: n(i,j) f(i, j)u(i,j), where f is the noise free image and where u is a zero-mean Gaussian noise with standard deviation . The computer simulated image or noisy image g is then given by g(i,j) f(i, j) n(i, j) .

Contrast levels of noise free images were in the range [10%; 60%] with a step size of 10%. Background grey-level was set to 128. Concerning noisy images, standard deviation of the zero-mean Gaussian noise u was adjusted so that the signal to noise ratio (SNR) takes the values 21dB, 15dB and 9dB which simulate a low, an intermediate and a high noise level respectively. Computer simulated images consist of 256256-pixels for microcalcification groups and nodules, and 336336-pixels for fibres. The whole computer simulated images were coded on 256 grey levels.

Two criteria are used to test the effectiveness of the algorithm on computer simulated images. The first one, namely output to input Signal to Noise Ratio (SNR) , quantifies noise suppression, and the second one, namely the mean-squared error (MSE), in addition to quantify noise removal gives also an information on structure distortion and therefore better interprets the first criterion. One can notice that parameter is all the more higher as the method removes much more noise, whereas MSE parameter is all the smaller as the method denoises and preserves structures in the image.

Results obtained on simulated images show that the trigonometric function ( ) tan <sup>4</sup> *x x*

gives the best balance between noise reduction and edge sharpness preservation and that( ) *x x* is suitable for contrast enhancement.

*Mb <sup>i</sup> <sup>j</sup> Eij C i <sup>j</sup>* if Mb(i,j) < Mc(i,j)

This transform gives a new image E which is an enhanced version of image I. It is then possible to evaluate the efficiency of the method from comparison between images E and I.

Several functions including square root, exponential, polynomial and trigonometric were tested (Guis et al., 2003). Actually functions which are over the line y=x increase the contrast but enhance the noise too. In the other hand, functions which are under the line y=x yield noise reduction. Because of the noisy nature of real images of phantom, the second kind of functions was chosen for enhancing the objects contained in these images. To choose suitable function , computer simulated images containing objects similar to those observed in the phantom film were generated with various contrast and noise levels. The aim of this simulation was to perform a quantitative evaluation of the noise reduction method described in previous sections. For each target, 6 noise-free images were generated each of them with a different contrast level. Three noise levels were then assigned to each contrast level image. Contrast level is defined as the difference between the mean grey level of the object and the mean grey level of the background divided by the mean grey level of the background. According to studies on radiographic noise, two types of noise sources, namely film granularity and quantum mottle, are present in an X-ray image. Spatially correlated Poisson noise model has to be considered in the case of mammographic films. In our simulations, a signal-dependent spatially uncorrelated Gaussian noise is used as a firstorder approximation of the Poisson noise model (Quian et al., 1994; Aghadasi et al., 1992; Kuan et al., 1985) namely: n(i,j) f(i, j)u(i,j), where f is the noise free image and where u is a zero-mean Gaussian noise with standard deviation . The computer simulated image or

Contrast levels of noise free images were in the range [10%; 60%] with a step size of 10%. Background grey-level was set to 128. Concerning noisy images, standard deviation of the zero-mean Gaussian noise u was adjusted so that the signal to noise ratio (SNR) takes the values 21dB, 15dB and 9dB which simulate a low, an intermediate and a high noise level respectively. Computer simulated images consist of 256256-pixels for microcalcification groups and nodules, and 336336-pixels for fibres. The whole computer simulated images

Two criteria are used to test the effectiveness of the algorithm on computer simulated images. The first one, namely output to input Signal to Noise Ratio (SNR) , quantifies noise suppression, and the second one, namely the mean-squared error (MSE), in addition to quantify noise removal gives also an information on structure distortion and therefore better interprets the first criterion. One can notice that parameter is all the more higher as the method removes much more noise, whereas MSE parameter is all the smaller as the method

Results obtained on simulated images show that the trigonometric function ( ) tan <sup>4</sup>

gives the best balance between noise reduction and edge sharpness preservation and

*x x*

(, ) , (1 '( , ))

**3.2.1 Performance evaluation on simulated images**

noisy image g is then given by g(i,j) f(i, j) n(i, j) .

denoises and preserves structures in the image.

( ) *x x* is suitable for contrast enhancement.

were coded on 256 grey levels.

that

Fig. 5. Correction of Background inhomogeneity on extracted subimages. (a), (d) and (g) : original extracted subimages; (b), (e) and (h) background images; (c), (f) and (i) corrected images.

#### **3.2.2 Preprocessing of real phantom images**

Before applying noise reduction and contrast enhancement steps, inhomogeneous background of subimages containing microcalcification is extracted using the classical multiresolution Burt decomposition into level 3 (level 0 is the original image). A linear interpolation is then applied to obtain background image. The same method was not suitable to correct background for subimages containing masses and fibres, due to the object to image size ratio. Using the local contrast modification method described above with a big window size for Wmax, enabled us to obtain background image. At last corrected image for the whole objects is obtained by subtracting the background image from the original image as shown in Fig. 5.

Applying the noise reduction and contrast enhancement steps described in the previous section, yield the images shown in Fig. 6.

Mammographic Quality Control Using Digital Image Processing Techniques 531

consisted in defining around each detected object, a window centered on it and using a threshold based on the computation of the mean and standard deviation of pixels within

cross-correlation

global thresholding

connected component Labelling

 microcalcifications extraction

Fig. 7. General scheme of microcalcification group segmentation. (1) Mmic : template image, (2) Inet : resulting image after noise reduction and contrast enhancement, (3) Ic : resulting image after cross-correlation,(4) Iseuil : thresholded cross-correlated image, (5) Ietiq : connected component labelled image, (6) Ifin : resulting image with detected microcalcifications.

this window. Fig 8 shows an example of microcalcifications group segmentation.

(2) Inet

(3) Ic

(4) Iseuil

(5) Ietiq

(6) Ifin

(1) Mmic

Fig. 6. Noise reduction and contrast enhancement steps on extracted subimages (a), (d) and (g): original extracted subimages; (b), (e) and (h) denoised images; (c), (f) and (i) contrast enhanced images.

#### **4. Segmentation of extracted subimages**

#### **4.1 Microcalcification segmentation case**

Microcalcifications segmentation was based on the computation of a cross-correlation between a template image Mmic and the preprocessed resulting image after noise reduction and contrast enhancement Inet. The different steps of microcalcification groups are summerized in figure 7. The template image was built after a supervised learning on real phantom images. A global thresholding was then applied on the thresholded image for extracting microcacifications. The connected component labelling step is done to determine the number of detected objects in Iseuil image. The microcalcification extraction step

(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

Fig. 6. Noise reduction and contrast enhancement steps on extracted subimages (a), (d) and (g): original extracted subimages; (b), (e) and (h) denoised images; (c), (f) and (i) contrast

Microcalcifications segmentation was based on the computation of a cross-correlation between a template image Mmic and the preprocessed resulting image after noise reduction and contrast enhancement Inet. The different steps of microcalcification groups are summerized in figure 7. The template image was built after a supervised learning on real phantom images. A global thresholding was then applied on the thresholded image for extracting microcacifications. The connected component labelling step is done to determine the number of detected objects in Iseuil image. The microcalcification extraction step

enhanced images.

**4. Segmentation of extracted subimages**

**4.1 Microcalcification segmentation case**

consisted in defining around each detected object, a window centered on it and using a threshold based on the computation of the mean and standard deviation of pixels within this window. Fig 8 shows an example of microcalcifications group segmentation.

Fig. 7. General scheme of microcalcification group segmentation. (1) Mmic : template image, (2) Inet : resulting image after noise reduction and contrast enhancement, (3) Ic : resulting image after cross-correlation,(4) Iseuil : thresholded cross-correlated image, (5) Ietiq : connected component labelled image, (6) Ifin : resulting image with detected microcalcifications.

Mammographic Quality Control Using Digital Image Processing Techniques 533

Fig. 9. Segmentation of a mass subimage (a): extracted subimage after noise reduction and contrast enhancement; (b): Thresholded image. (c): Initialization of active contour. (d) First iteration of the active contour. (e): Second iteration of active contour. (f): final segmentation.

2

(d) (e) (f)

(a) (b) (c)

(a) (b)

<sup>1</sup>

Fig. 10. Template images for fibre segmentation.

Fig. 8. Segmentation of microcalcifications. (a): extracted subimage after noise reduction and contrast enhancement Inet ; (b): result of template matching Ic; (c): connected component labelled image Ietiq ; (d): resulting image Ifin.

#### **4.2 Mass segmentation case**

Mass segmentation was done by using an active contour. First a square was set as an initial contour and the energy used depended only on image gradient. The algorithm used for that purpose is described as follows:

Step 1: Each point i of the active contour evolved along the normal of segment (i-1,i+1) until it met a mass edge.

Step 2: When the four initial points reached the mass edges, other points were added between each couple of points (i and i+1).

Step 3: Each added point in the previous step evolved as initials points in step 1.

The algorithm stopped when a fixed but great number of iterations was reached. Fig. 9 shows an example of a mass segmentation.

#### **4.3 Fibre segmentation case**

As for microcalcifications, fibre segmentation used a template matching between two template images (see Fig. 10) and the preprocessed resulting fibre image after applying noise reduction and contrast enhancement steps. An automatic global thresholding is then used, followed by a logical filter OR and a connected component labelling step. Figs. 11 and 12 show the general scheme of a fibre segmentation and an example of fibre segmentation respectively.

(a) (b)

(c) (d)

Fig. 8. Segmentation of microcalcifications. (a): extracted subimage after noise reduction and contrast enhancement Inet ; (b): result of template matching Ic; (c): connected component

Mass segmentation was done by using an active contour. First a square was set as an initial contour and the energy used depended only on image gradient. The algorithm used for that

Step 1: Each point i of the active contour evolved along the normal of segment (i-1,i+1) until

Step 2: When the four initial points reached the mass edges, other points were added

The algorithm stopped when a fixed but great number of iterations was reached. Fig. 9

As for microcalcifications, fibre segmentation used a template matching between two template images (see Fig. 10) and the preprocessed resulting fibre image after applying noise reduction and contrast enhancement steps. An automatic global thresholding is then used, followed by a logical filter OR and a connected component labelling step. Figs. 11 and 12 show the general scheme of a fibre segmentation and an example of fibre segmentation

Step 3: Each added point in the previous step evolved as initials points in step 1.

labelled image Ietiq ; (d): resulting image Ifin.

between each couple of points (i and i+1).

shows an example of a mass segmentation.

**4.2 Mass segmentation case**

purpose is described as follows:

**4.3 Fibre segmentation case**

it met a mass edge.

respectively.

Fig. 9. Segmentation of a mass subimage (a): extracted subimage after noise reduction and contrast enhancement; (b): Thresholded image. (c): Initialization of active contour. (d) First iteration of the active contour. (e): Second iteration of active contour. (f): final segmentation.

Fig. 10. Template images for fibre segmentation.

Mammographic Quality Control Using Digital Image Processing Techniques 535

(a) (b)

(c) (d)

(e) (f)

(g) (h)

Fig. 12. Segmentation of a fibre subimage. (a): extracted subimage after noise reduction and contrast enhancement; (b): Thresholded image (c): Result of template matching with template image in Fig. 10(a); (d): Result of template matching with template image in Fig.

thresholding image (d); (g): Resulting image after applying OR logic filter on images (e) and

10(b); (e): Resulting image after thresholding image (c); (f): Resulting image after

(f); (h): Connect component labelled image.

Fig. 11. General scheme of fibre segmentation. (1) Inet : resulting image after noise reduction and contrast enhancement , (2) Iseuil : image Inet thresholded , (3) Mfib1 : template image 1, (4) Mfib2 : template image 2, (5) Ic,1 : obtained image after cross correlation between Mfib1 and Iseuil , (6) Ic,2 : obtained image after cross correlation between Mfib2 and Iseuil (7) Ic,1,s : image Ic,1 thresholded , (8) Ic,2,s : image Ic,2 thresholded , (9) Iadd : resulting image after logical filter OR between Ic,1,s and Ic,2,s (10) Ietiq : connected component labelled image.

global thresholding on histogram

**Images processing**

cross-correlation

(2) Iseuil

(1) Inet

(3) Mfib1

(5) Ic,1

(7) Ic,1,s

(9) Iadd

(10) Ietiq

Fig. 11. General scheme of fibre segmentation. (1) Inet : resulting image after noise reduction and contrast enhancement , (2) Iseuil : image Inet thresholded , (3) Mfib1 : template image 1, (4) Mfib2 : template image 2, (5) Ic,1 : obtained image after cross correlation between Mfib1 and Iseuil , (6) Ic,2 : obtained image after cross correlation between Mfib2 and Iseuil (7) Ic,1,s : image Ic,1 thresholded , (8) Ic,2,s : image Ic,2 thresholded , (9) Iadd : resulting image after logical filter OR

connected component labelling

global thresholding

OR logical filter

(4) Mfib2

**Images**

(6) Ic,2

(8) Ic,2,s

between Ic,1,s and Ic,2,s (10) Ietiq : connected component labelled image.

(e) (f)

Fig. 12. Segmentation of a fibre subimage. (a): extracted subimage after noise reduction and contrast enhancement; (b): Thresholded image (c): Result of template matching with template image in Fig. 10(a); (d): Result of template matching with template image in Fig. 10(b); (e): Resulting image after thresholding image (c); (f): Resulting image after thresholding image (d); (g): Resulting image after applying OR logic filter on images (e) and (f); (h): Connect component labelled image.

Mammographic Quality Control Using Digital Image Processing Techniques 537

This chapter presents a feasibility study of automating breast phantom scoring using image processing techniques. The main contribution in this project is noise reduction and contrast enhancement of noisy images extracted from digitized phantom films. The segmentation step which uses known methods shows that quality control in mammographic facilities could be done using image processing techniques. Next step in this project is to adapt image processing techniques used for digitized film to digital phantom images acquired directly from Full-Filed Digital Mammograms. In this case it will be possible to control the quality of digital mammographic systems using software

K. W. Brooks, J. H. Trueblood, K. J. Kearfott, D. T. Lawton "Automated analysis of the

D. P. Chakraborty "Computer analysis of mammography phantom images (CAMPI).

G. Dougherty "Computerised evaluation of mammographic image quality using phantom images". Computerized Medical Imaging and Graphics 22: 365-373, 1998. A. D. Castellano Smith, I. A. Castellano Smith, D. R. Dance. "Objective assessment of

L. Blot, A. Davis, M. Holubinka ,R. Marti, R. Zwiggelaar "Automated quality assurance

P. Mayo, F. Rodenas, G. Verdu, J. I. Villaescusa, J. M. Campayo. "Automatic evaluation of

A. P. Dhawan, G. Buelloni R. Gordon, "Enhancement of mammographic features by optimal

A. P. Dhawan E. Le Royer "Mammographic feature enhancement by computarized image

V. Guis, M. Adel, M. Rasigni, G. Rasigni, B. Seradour, P. Heid "Adaptive neighborhood

W. Qian, L.P. Clarke, M. Kallergi, R. A. Clark, "Tree-Structured Nonlinear Filters in Digital

F. Aghadasi, R. K. Ward, B. Palcic, "Noise filtering for mammographic images", *14th Annual*

Mammography". IEEE Trans. Medical Imaging 13(1), 25-36, 1994.

acquired digital images". Medical Physics 24 : 1269-1277, 1997.

American College of Radiology mammographic accreditation phantom images".

Application to the measurement of microcalcification image quality of directly

phantom image quality in mammography: a feasibility study". The British Journal

applied to mammographic imaging". EURASIP Journal on Applied Signal

the image quality of a mammographic phantom". Computers Methods and

adaptative neighborhood image processing," IEEE Trans. Medical Imaging 5(1), 8-

processing". Computer Methods and Programs in Biomedecine 27, 23-25,

contrast enhancement in mammographic phantom images". Optical Engineering

*International Conference of the I.E.E.E. Engineering in Medecine and Biology Society*

**6. Conclusion**

**7. References**

similar to the one described in this study.

Medical Physics 24 : 907-923, 1997.

of Radiology 71 : 48-58, 1998

Processing 7 : 736-745, 2003

15,1986.

1988.

42: 357-366, 2003.

1877-1878, 1992.

Programs in Biomedicine 73, 115-128, 2004

## **5. Results and discussion**

Nine phantom images from different mammographic facilities were tested. For each phantom image, only 4 subimages of each target were extracted. Two main reasons leaded us to do this choice: first, readers could not detect more than 4 objects on the phantoms used in our study, and second, a mammographic facility is considered to have good quality phantom films if at least 4 objects are detected from each embedded target.

216 microcalcifications (36 microcalcification groups) were studied. The most prominent microcalcification group M1 and M2 were almost all detected. Microcalcifications that were not detected were those with poor contrast. False detections were due to film emulsion tearing and appeared on M3 and M4 groups. Table 1 summarizes results obtained on these nine phantom films.

Thirty six masses and 36 fibres were studied. Three masses among 36 were not detected because of the non convergence of the iterative active contour algorithm. This appeared on masses containing holes when being preprocessed. When better initialising the active contour, it was possible to detect the whole masses.

The whole fibres were detected but as seen in Fig 12 some other objects appeared on the final segmentation image. These small objects will be removed in a further processing.

## **6. Conclusion**

536 Modern Approaches To Quality Control

Nine phantom images from different mammographic facilities were tested. For each phantom image, only 4 subimages of each target were extracted. Two main reasons leaded us to do this choice: first, readers could not detect more than 4 objects on the phantoms used in our study, and second, a mammographic facility is considered to have good quality

216 microcalcifications (36 microcalcification groups) were studied. The most prominent microcalcification group M1 and M2 were almost all detected. Microcalcifications that were not detected were those with poor contrast. False detections were due to film emulsion tearing and appeared on M3 and M4 groups. Table 1 summarizes results obtained on these

Thirty six masses and 36 fibres were studied. Three masses among 36 were not detected because of the non convergence of the iterative active contour algorithm. This appeared on masses containing holes when being preprocessed. When better initialising the active

The whole fibres were detected but as seen in Fig 12 some other objects appeared on the final segmentation image. These small objects will be removed in a further processing.

*1 2 3 4 5 6 7 8 9*

*number* **6 6 6 6 6 6 6 6 6**

*number* **6 6 6 6 6 6 6 6 6**

*number* **6 6 6 6 5 6 5 6 5**

*number* **6 6 6 6 3 6 5 4 6**

*False detection* **3 3 2 1 1**

*False detection* **1 1 3**

*False detection* **5 4 1 3 2**

phantom films if at least 4 objects are detected from each embedded target.

contour, it was possible to detect the whole masses.

*Detected microcalcifications*

*False detection*

*Detected microcalcifications*

*Detected microcalcifications*

*Detected microcalcifications*

Table 1. Detection results on microcalcification groups.

**5. Results and discussion**

nine phantom films.

M1 group

M2 group

M3 group

M4 group

This chapter presents a feasibility study of automating breast phantom scoring using image processing techniques. The main contribution in this project is noise reduction and contrast enhancement of noisy images extracted from digitized phantom films. The segmentation step which uses known methods shows that quality control in mammographic facilities could be done using image processing techniques. Next step in this project is to adapt image processing techniques used for digitized film to digital phantom images acquired directly from Full-Filed Digital Mammograms. In this case it will be possible to control the quality of digital mammographic systems using software similar to the one described in this study.

## **7. References**

D. T. Kuan, A. A. Shawchuk, T. C. Strand, P. Chavel, "Adaptive noise smoothing filter for images with signal-dependent noise". IEEE Trans. Patt. Anal. Mach. Intell*.* 7(2), 165- 177, 1985.

D. T. Kuan, A. A. Shawchuk, T. C. Strand, P. Chavel, "Adaptive noise smoothing filter for

177, 1985.

images with signal-dependent noise". IEEE Trans. Patt. Anal. Mach. Intell*.* 7(2), 165-

## *Edited by Ahmed Badr Eldin*

Rapid advance have been made in the last decade in the quality control procedures and techniques, most of the existing books try to cover specific techniques with all of their details. The aim of this book is to demonstrate quality control processes in a variety of areas, ranging from pharmaceutical and medical fields to construction engineering and data quality. A wide range of techniques and procedures have been covered.

Photo by olm26250 / iStock

Modern Approaches To Quality Control

Modern Approaches To

Quality Control

*Edited by Ahmed Badr Eldin*


**3. Mammary tissue–derived primary cells as an** *in vitro* **model**

Mammary gland development occurs in stages; proliferation and differentiation of mammary cells are dependent on sexual development and reproduction, which are under control of endocrine system [12]. The gland is primarily composed of mesenchymal and epithelial tissue and the latter is subjected to significant remodeling during lactation cycles. The tissue remodeling involves proliferation and differentiation of the epithelial cells forming functional glandular tissue, followed by regression of the tissue due to apoptosis and loss of glandular structure and function (involution). The functional part of mammary gland is glandular tissue connected by a branched system of secondary ducts, combining into larger primary ducts, which end in the gland cistern. The milk is synthesized by secretory luminal cells arranged in spherical structures called alveoli, which form larger structures called lobules. Alveoli and ducts of lactating mammary gland are composed of different types of epithelial cells important for milk synthesis and secretion, while connective and fat tissue surround and support the epithelial structures. An alveolus is comprised of a single layer of milk secreting luminal epithelium, surrounded by a single layer of contractile myoepithelial cells, which lie adjacent to the basal membrane, where mammary stem/progenitor cells also reside [10].

In agreement with the "Replacement" of the three Rs principle (3Rs: Replacement, Reduction, and Refinement) adequate *in vitro* model, mimicking the function of the mammary gland allows the study of physiological, biochemical, and immune functions of the mammary gland, substituting *in vivo* experiments. In addition to the ethical issues, cell lines enable use of many technical replicas, better control of the environment, and surmount the problem of variation introduced by animal's individuality [13] and the problem of systemic effects, which makes elucidation of a contribution of a particular cell type of interest difficult [14]. A main limitation of primary cell models is a finite life span, and a limiting number of available biological replicas, as a derived cell culture, represent a single genotype, while establishment and characterization of a large number of cell cultures/lines are quite a challenging and laborious process. Additionally, cell cultures do not always properly model *in vivo* conditions; therefore, limitations should be considered for each individual purpose.

Several ruminant immortalized mammary epithelial cell lines as MAC-T [15] and BME-UV [16] were generated by genomic integration of Simian virus large T-antigen (SV40LTA). However, transformed mammary cells are genetically and (usually) phenotypically changed. Transient mammary lines show low responsiveness to lactogenic hormones [17] and are not proper approximation of *in vivo* lactation. Genetic modifications and adaptations to growth in cell cultures alter metabolic pathways in continuous cell lines; therefore, the use of primary cells is a much better approximation of the *in vivo* system [18].