Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation . The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks ( FCN ) . Despite their success , these models have two limitations : ( 1 ) their optimal depth is apriori unknown , requiring extensive architecture search or inefficient ensemble of models of varying depths ; and ( 2 ) their skip connections impose an unnecessarily restrictive fusion scheme , forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks . Liver and hepatic tumor segmentation remains a challenging problem in Computer Tomography ( CT ) images analysis due to its shape variation and vague boundary . The general hypothesis says that deep learning methods produce improved results on medical image segmentation . This paper formulates the segmentation of liver tumor in CT abdominal images as a classification problem , and then solves it using a cascaded classifier framework based on deep convolutional neural networks . BACKGROUND Segmentation of anatomical structures of the heart from cardiac magnetic resonance images ( MRI ) has a significant impact on the quantitative analysis of the cardiac contractile function . Although deep convolutional neural networks ( ConvNets ) have achieved considerable success in medical imaging segmentation , it is still a challenging task for existing deep ConvNets to precisely and automatically segment multiple heart structures from cardiac MRI . This paper presents a novel recurrent interleaved attention network ( RIANet ) to comprehensively tackle this issue . Statistical shape modeling is widely used for medical image segmentation and interpretation . The main problem in building a shape model is the construction of a pointwise correspondence between the training objects . Statistical shape models ( SSMs ) are well-established tools for medical image segmentation and interpretation due to their ability to constrain the image analysis result within allowable limits as defined by a representative set of training examples . However , the construction of SSMs that are rich , i.e. , that represent well the natural and complex variability of anatomical structures , is a difficult task , as this is often limited by the available delineated training shapes , which rarely exceed a few dozen . Abstract The development of novel noise-reduction algorithms is one of the main research areas in the field of medical diagnostic X-ray imaging . The purpose of this study is to develop an improved total-variation ( TV ) noise-reduction technique using a gradient method that employs an iteration counter , and confirm its feasibility for applications in chest and abdominal X-ray imaging systems . Mathematical model of medical expertise is formulated as a function used to search for special features of pixels that are representing respiratory diseases like pneumonia , lungs sarcoidosis and cancer . Presented decision modeling simulates medical x-ray image examination process to show where potentially diseased tissues are located . To enhance decision support the system returns to the doctor detection results from two tracks . Compared with the start-of-art energy integration detectors ( EIDs ) , photon-counting detectors ( PCDs ) with energy discrimination capabilities have demonstrated great potentials in various applications of medical x-ray radiography and computed tomography ( CT ) . However , the advantages of PCDs may be compromised by various hardware-related degradation factors . One of the main challenges in low dose x-ray computed tomography ( CT ) is the presence of highly structured noise . Model based iterative reconstruction methods ( MBIR ) have shown great potential to overcome this problem ; however , they have also introduced an additional challenge : highly nonlinear behavior . This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model . An example of such a linear filter is presented for low-dose X-ray computed tomography ( CT ) . Low-dose computed tomography ( CT ) reconstruction is a significant concern in CT imaging field . Currently , besides CT manufacturers adapted hardware techniques and optimized scan protocols to reduce the X-ray dose , algorithm-based low-dose CT reconstruction methods have been exploited extensively . Statistical iterative reconstruction ( SIR ) algorithms have shown advantages over the conventional filtered back-projection method for low-dose computed tomography ( CT ) reconstruction . For the SIR algorithms , the regularization term plays a critical role on determining the performance . Pre-processing the noisy sinogram before reconstruction is an effective and efficient way to solve the low-dose X-ray Computed Tomography ( CT ) problem . The objective of this paper is to develop a low-dose CT image reconstruction method based on statistical sonogram smoothing approach . Total variation ( TV ) regularization is one of popular techniques for low dose x-ray computed tomography image reconstruction . However , the reconstruction image by TV method often suffers staircase effect . In this paper , we propose an edge sparsity model , which penalizes the difference between L1 norm and L2 norm of gradient , for low dose x-ray computed tomography image reconstruction . Alternating direction method of multipliers ( ADMM ) is adopted to solve the proposed model . Although lower the X-ray tube current ( mAs ) is an easy way to reduce the radiation dose directly , the associated reconstructed image quality will degrade significantly due to the excessive noise in projection data . For high quality low-dose CT reconstruction , the projection data restoration methods can adaptively suppress the noise in the highest attenuation regions . However , these methods remove not only the noise but also some edge information , which result in the reconstructed image suffers noticeable resolutions loss . To address such two issues , a weighted ramp filter is investigated to reduce the amplification of high frequency noise , and then a total-variation based backprojection regularization ( BPR ) method is developed to mitigate the DC shift and improve the robustness to noise . The experimental results show that BPR outperforms FBP for low-dose CT imaging reconstruction . Low-dose X-ray computed tomography ( CT ) imaging is desirable for various clinical applications due to the growing concerns about excessive radiation exposure to the patients . One strategy to achieve low-dose CT imaging is to lower the number of projection views per rotation during data acquisition . When excessive quantum noise is present in extremely low dose X-ray CT imaging , statistical properties of the data has to be considered to achieve a satisfactory image reconstruction . Statistical iterative reconstruction with accurate modeling of the noise , rather than a filtered back-projection ( FBP ) with low-pass filtering , is one way to deal with the problem . In the past , this has been considered as the fundamental resolution limit in many imaging problems . However , with the recent theory of compressive sampling (CS),1–3 it is now possible to overcome the Nyquist limit for the accurate recovery of unknown sparse signals from underdetermined Fourier measurements in various imaging applications , e.g. , accelerated magnetic resonance imaging ( MRI ) , low-dose xray computed tomography , and optics . To facilitate more robust reconstruction , recovery algorithms usually take the form of a penalized least squares or a constrained optimization framework ( which are dependent on the specific signal representation and discretization scheme , rather than the nature of the original problem in the continuous domain ) . While the shape energy draws the level set function towards a range of possible prior shapes , the edge-mounted Willmore energy captures the localized geometry information and smooths the surface during the level set evolution . Experimental results on segmentation of spinal vertebrae from CT images demonstrate the powerful combination of prior knowledge and geometrical flow . Precise automatic vertebra segmentation in computed tomography ( CT ) images is important for the quantitative analysis of vertebrae-related diseases but remains a challenging task due to high variation in spinal anatomy among patients . In this paper , we propose a deep learning approach for automatic CT vertebra segmentation named patch-based deep belief networks ( PaDBNs ) . Our proposed PaDBN model automatically selects the features from image patches and then measures the differences between classes and investigates performance . However , automated detection and segmentation are challenging tasks due to a relatively high degree of anatomical complexity , presence of unclear boundaries and articulation of vertebrae with each other . In this paper , we describe a sparse representation error minimization ( SEM ) framework for joint detection and segmentation of vertebral bodies in CT images . By minimizing the sparse representation error of sampled intensity values , we are able to recover the oriented bounding box ( OBB ) and segmentation binary mask for each vertebral body in the CT image . The localization and identification of vertebrae in spinal CT images plays an important role in many clinical applications , such as spinal disease diagnosis , surgery planning , and post-surgery assessment . However , automatic vertebrae localization presents numerous challenges due to partial visibility , appearance similarity of different vertebrae , varying data quality , and the presence of pathologies . PurposeIn the last decades , the increasing medical interest in magnetic resonance imaging ( MRI ) of the spine gave rise to a growing number of publications on computerized methods for spine analysis , covering goals such as localization and segmentation of vertebrae and intervertebral discs as well as the extraction and segmentation of the spinal canal and cord . We provide a critical systematic review to work in the field , putting focus on approaches that can be applied to different imaging sequences and settings . In this paper , we proposed a mathod for lumbar vertebra CT image segmentation based on the contourlet transform and artificial neural networks(ANNs ) . The proposed method consists of three portions . Precise detection and segmentation of spinal vertebrae are crucial in the study of spinal related disease or disorders such as vertebral fractures . Identifying severity of fractures and understanding its causes will help physicians determine the most effective pharmacological treatments and clinical management strategies for spinal disorders . Our proposed approach has been tested on 115 vertebrae CT volumes of various patients . Quantitative comparisons validate that our proposed AGLSA is more accurate in segmenting lumbar vertebrae CT images with irregular boundaries and more robust to various levels of salt-and-pepper noise . In most cases with medical imaging , classical inverse Radon transforms , such as an inverse Fourier transform for MRI , work well for recovering clean images from the measured data . Unfortunately in the case of X-Ray CT , where undersampled data is very common , more than this is needed to resolve faithful and usable images . In this paper , we explore the history of classical methods for solving the inverse problem for X-Ray CT , followed by an analysis of the state of the art methods that utilize supervised deep learning . Such topics are usually not addressed across the different imaging modalities in one book , and this is a notable strength of the treatment given here . Table of Contents : Introduction / Diagnostic X-Ray Imaging / X-Ray CT / Ultrasonics / Pulse-Echo Ultrasonic Imaging / Doppler Velocimetry / An Introduction to MRI Thus , this method can interface with low resolution imaging techniques such as Small Angle X-Ray Scattering and Cryo-Electron Microscopy . In this thesis , I show how to construct a finite element mesh from both of these sources and run simulations to replicate the results from Small Angle X-Ray Scattering and Cryo-Electron Microscopy experiments . In more detail , I have taken a structure obtained using Small Angle X-ray Scattering , ran simulations and checked that the dynamics do not affect the average X-Ray scattering curve . Augmented reality X-Ray Vision is an emerging concept . While AR deals with virtual and real objects coexisting in the same space , AR X-Ray Vision is a subdivision of the broad spectrum of AR , which provides a " see through " vision among real world objects . In this paper , we have thoroughly analysed the existing methodologies dealing with AR X-Ray Vision and we have come up with a convenient method that enables easy implementation . This paper deals with creating a methodology to provide an X-Ray vision using the anaglyph technique and finally integrating it with the Leap Motion Controller to enable gesture interaction to move the window around through which the point of interest can be viewed . All compound , multi-pane , and non-radiology images were eliminated using an automatic binary classifier fine-tuned with a deep convolutional neural network system . Radiology Objects in COntext ( ROCO ) dataset contains over 81k radiology images with several medical imaging modalities including Computer Tomography , Ultrasound , X-Ray , Fluoroscopy , Positron Emission Tomography , Mammography , Magnetic Resonance Imaging , Angiography . All images in ROCO have corresponding caption , keywords , Unified Medical Language Systems Concept Unique Identifiers and Semantic Type . Python Environments Running a Python Program Basic Python Statements and Data Types Computing using Python Modules Introduction Python Modules Numpy Scipy Matplotlib Python Imaging Library Scikits Python OpenCV Module Image and Its Properties Introduction Image and Its Properties Image Types Data Structures for Image Analysis Programming Paradigm Image Processing using Python Spatial Filters Introduction Filtering Edge Detection using Derivatives Image Enhancement Introduction Pixel Transformation Image Inverse Power Law Transformation Log Transformation Histogram Equalization Contrast Stretching Fourier Transform Introduction Definition of Fourier Transform Two-Dimensional Fourier Transform Convolution Filtering in Frequency Domain Segmentation Introduction Histogram-Based Segmentation Region-Based Segmentation Segmentation Algorithm for Various Modalities Morphological Operations Introduction History Dilation Erosion Grayscale Dilation and Erosion Opening and Closing Hit-or-Miss Thickening and Thinning Image Measurements Introduction Labeling Hough Transform Template Matching Image Acquisition X-Ray and Computed Tomography Introduction History X-Ray Generation Material Properties X-Ray Detection X-Ray Imaging Modes Computed Tomography ( CT ) Hounsfield Unit ( HU ) Artifacts Magnetic Resonance Imaging Introduction Laws Governing NMR and MRI Material Properties NMR Signal Detection MRI Signal Detection or MRI Imaging MRI Construction T1 , T2 , and Proton Density Image MRI Modes or Pulse Sequence MRI Artifacts Light Microscopes Introduction Physical Principles Construction of a Wide-Field Microscope Epi-Illumination Fluorescence Microscope Confocal Microscopes Nipkow Disk Microscopes Confocal or Wide-Field ? Cover image : X-ray images of woods ( series N0010 colored with ‘ hot ’ colormap ) . © Springer International Publishing Switzerland 2015 D. Mery , Computer Vision for X-Ray Testing , DOI 10.1007/978 - 3 - 319 - 20747 - 6_1 1 We show the maximum gap length , dmax , on square and hexagonal lattices is Thanks are due to Graham Farr , Computer Science , Monash University , for pointing us to the literature on finding minimal vectors in lattices . IS acknowledges support from the Centre for X-Ray Physics and Imaging for this project . AK is a Monash University postgraduate student in receipt of an Australian Postgraduate Award scholarship , provided through the Australian Government . Fine alignment of x-ray optics using wavefront sensor measurements . International Workshop on X-Ray Metrology . http://antoine.wojdyla.fr/assets/posters/iwxm2018.pdf The purpose of this paper is to identify the threats that exist in Healthcare Information Systems ( HIS ) . The study has been carried out in three different departments namely , Information Technology Department ( ITD ) , Medical Record Department ( MRD ) and X-Ray Department in one of the leading government supported hospital in Malaysia . The hospital was equipped with Total Hospital Information System ( THIS ) environment . For example , when dealing with brain Magnetic Resonance ( MR ) images as a biometric tool , no one can modify deliberately features of his own brain even if these features can change over the time in case of pathological individuals , or during the growth period ( not considered in this study ) . In particular , a special interest is given to brain MRI and X-Ray based biometrics . Some examples using brain MRI images , Hand and Lung X-Ray are highlighted in this paper . Objective:3D(Three-dimensional ) lung segmentation is an indispensable step in pulmonary nodules computer-aided detection based CT images . In this paper , we proposed a simple and effective 3D segmentation method , which combined the threshold , connected regional mark and morphological techniques . Lung segmentation is an indispensable step in pulmonary nodules computer-aided detection based CT images . Aimed at decreasing the Snake dependence on initial contour points , improving the accuracy of segmenting the depth sag area and resistance of noise , this paper proposes an interactive lung parenchyma segmentation algorithm , which uses improved Live-Wire algorithm to define Snake initial contour . Finally , the final segmentation result is obtained by setting the threshold based on this paper method . After experimental comparison , this paper demonstrates the feasibility and effectiveness of convolution neural network in the segmentation of pulmonary CT images . With the ability to display anatomy free from superimposing structures and greater visual clarity , Multi-Detector-CT has shown to be more sensitive than the chest radiograph in identifying alterations of lung parenchyma . In automated evaluation of pulmonary CT-scans , quantitative image processing techniques are applied for objective evaluation of the data . A number of methods have been proposed in the past , most of which utilize simple densitometric tissue features based on the mean X-ray attenuation coefficients expressed in terms of Hounsfield Units [ HU ] . In this paper , in order to overcome this difficulty , the deep CNNs GoogleNet and ResNet are pre-trained on the large scale database ImageNet . The fully connected layers and the classifiers of the pre-trained networks are replaced to complete the classification of CT images of pulmonary nodules . A sub set of the Lung Image Database Consortium image collection ( LIDC-IDRI ) is used to fine-tune the network and validate the classification accuracy . Computer-aided diagnosis ( CAD ) has already been widely used in medical image processing . We recently make another trial to implement convolutional neural network ( CNN ) on the classification of pulmonary nodules of thoracic CT images . The biggest challenge in medical image classification with the help of CNN is the difficulty of acquiring enough samples , and overfitting is a common problem when there are not enough images for training . We implement the 10-folder cross validation ( CV ) to testify the robustness of the classification model we trained . The result demonstrates that the transfer learning of the LeNet-5 is good for classifying pulmonary nodules of thoracic CT images , and the average values of Top-1 accuracy are 97.041 % and 96.685 % respectively . We believe that our work is beneficial and has potential for practical diagnosis of lung nodules . We have created a content-based image retrieval framework for computed tomography images of pulmonary nodules . When presented with a nodule image , the system retrieves images of similar nodules from a collection prepared by the Lung Image Database Consortium ( LIDC ) . We present a novel algorithm for the registration of pulmonary CT scans . Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework . The goal of computerised medical image analysis and interpretation is to detect abnormal appearance of the imaged anatomy and to assist radiologists in identifying and integrating all the useful information available in an image ( Brown & McNitt-Gray , 2000 ) . There is a growing number of computer-aided diagnosis ( CAD ) systems aimed at automating the analysis of lung CT images and supporting diagnosis ( Uppaluri , et al. , 1999 ; Uchiyama et al. , 2003 ; Sluimer , 2005 ; Zrimec et al. , 2007 ; Tolouee et al. , 2008 ) . Uppaluri et al. ( 1999 ) presented a CAD system for detecting six lung tissue patterns using textural features . Detection and segmentation of fissures is useful in the clinical interpretation of CT lung images to diagnose the presence of pathologies in the human lungs . A new automated method based on marker-based watershed transformation has been proposed to segment the fissures considering its unique structure as a long connected component . Computed Tomography ( CT ) lung nodules segmentation is a challenging task for detecting the nodules from images . Medical experts segment the medical images manually , that is almost not clear which leads to false detection . Some evidence suggests that periodic screening tests with the CT of patients will help in reducing the mortality rate caused by the lung cancer . Acomplete and accurate computer aided diagnosis ( CAD ) system for detection of nodules in lung CT images consists of three main steps : extraction of lung parenchyma , candidate nodule detection and false positive reduction . While precise segmentation of lung region speed upthe detection process of pulmonary nodules by limiting the search area , in candidate nodule detection step we attempt to include all nodule like structures . In this study , we developed a novel system , called Gaze2Segment , integrating biological and computer vision techniques to support radiologists ’ reading experience with an automatic image segmentation task . During diagnostic assessment of lung CT scans , the radiologists ’ gaze information were used to create a visual attention map . Next , this map was combined with a computer-derived saliency map , extracted from the gray-scale CT images . Image registration of lung CT images plays a crucial role in estimating lung motion that is of great significance in image guided radiation therapy . However , the respiratory-induced deformation makes registration of lung CT images rather difficult and challenging . This paper proposes a novel method for image registration of lung CT scans . Our approach consists of a procedure for automatically establishing landmark correspondences in lung CT scan pairs and an elaborate variational image registration scheme . The proposed end-to-end DNN demonstrated better sensitivity and accuracy for the task compared to a two-step approach , in which the reconstruction and detection DNNs were trained separately . A significant reduction of false positive rate on suspicious lesions were observed , which is crucial for the known over-diagnosis in low-dose lung CT imaging . The images reconstructed by the proposed end-to-end network also presented enhanced details in the region of interest . The aim of the proposed work is to improve low-dose lung CT ( LDCT ) screening using the processing of weighted intensity averaging over large-scale neighborhoods ( WIA-LN ) . Both current and voltage reductions were considered for LDCT imaging . This paper presents a joint spatial-intensity-shape ( JSIS ) feature-based method for the segmentation of CT lung nodules . First , a volumetric shape index ( SI ) feature based on the second-order partial derivatives of the CT image is calculated .
Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation . The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks ( FCN ) . Despite their success , these models have two limitations : ( 1 ) their optimal depth is apriori unknown , requiring extensive architecture search or inefficient ensemble of models of varying depths ; and ( 2 ) their skip connections impose an unnecessarily restrictive fusion scheme , forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks .
Liver and hepatic tumor segmentation remains a challenging problem in Computer Tomography ( CT ) images analysis due to its shape variation and vague boundary . The general hypothesis says that deep learning methods produce improved results on medical image segmentation . This paper formulates the segmentation of liver tumor in CT abdominal images as a classification problem , and then solves it using a cascaded classifier framework based on deep convolutional neural networks .
BACKGROUND Segmentation of anatomical structures of the heart from cardiac magnetic resonance images ( MRI ) has a significant impact on the quantitative analysis of the cardiac contractile function . Although deep convolutional neural networks ( ConvNets ) have achieved considerable success in medical imaging segmentation , it is still a challenging task for existing deep ConvNets to precisely and automatically segment multiple heart structures from cardiac MRI . This paper presents a novel recurrent interleaved attention network ( RIANet ) to comprehensively tackle this issue .
Statistical shape modeling is widely used for medical image segmentation and interpretation . The main problem in building a shape model is the construction of a pointwise correspondence between the training objects .
Statistical shape models ( SSMs ) are well-established tools for medical image segmentation and interpretation due to their ability to constrain the image analysis result within allowable limits as defined by a representative set of training examples . However , the construction of SSMs that are rich , i.e. , that represent well the natural and complex variability of anatomical structures , is a difficult task , as this is often limited by the available delineated training shapes , which rarely exceed a few dozen .
Abstract The development of novel noise-reduction algorithms is one of the main research areas in the field of medical diagnostic X-ray imaging . The purpose of this study is to develop an improved total-variation ( TV ) noise-reduction technique using a gradient method that employs an iteration counter , and confirm its feasibility for applications in chest and abdominal X-ray imaging systems .
Mathematical model of medical expertise is formulated as a function used to search for special features of pixels that are representing respiratory diseases like pneumonia , lungs sarcoidosis and cancer . Presented decision modeling simulates medical x-ray image examination process to show where potentially diseased tissues are located . To enhance decision support the system returns to the doctor detection results from two tracks .
Compared with the start-of-art energy integration detectors ( EIDs ) , photon-counting detectors ( PCDs ) with energy discrimination capabilities have demonstrated great potentials in various applications of medical x-ray radiography and computed tomography ( CT ) . However , the advantages of PCDs may be compromised by various hardware-related degradation factors .
One of the main challenges in low dose x-ray computed tomography ( CT ) is the presence of highly structured noise . Model based iterative reconstruction methods ( MBIR ) have shown great potential to overcome this problem ; however , they have also introduced an additional challenge : highly nonlinear behavior .
This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model . An example of such a linear filter is presented for low-dose X-ray computed tomography ( CT ) .
Low-dose computed tomography ( CT ) reconstruction is a significant concern in CT imaging field . Currently , besides CT manufacturers adapted hardware techniques and optimized scan protocols to reduce the X-ray dose , algorithm-based low-dose CT reconstruction methods have been exploited extensively .
Statistical iterative reconstruction ( SIR ) algorithms have shown advantages over the conventional filtered back-projection method for low-dose computed tomography ( CT ) reconstruction . For the SIR algorithms , the regularization term plays a critical role on determining the performance .
Pre-processing the noisy sinogram before reconstruction is an effective and efficient way to solve the low-dose X-ray Computed Tomography ( CT ) problem . The objective of this paper is to develop a low-dose CT image reconstruction method based on statistical sonogram smoothing approach .
Total variation ( TV ) regularization is one of popular techniques for low dose x-ray computed tomography image reconstruction . However , the reconstruction image by TV method often suffers staircase effect . In this paper , we propose an edge sparsity model , which penalizes the difference between L1 norm and L2 norm of gradient , for low dose x-ray computed tomography image reconstruction . Alternating direction method of multipliers ( ADMM ) is adopted to solve the proposed model .
Although lower the X-ray tube current ( mAs ) is an easy way to reduce the radiation dose directly , the associated reconstructed image quality will degrade significantly due to the excessive noise in projection data . For high quality low-dose CT reconstruction , the projection data restoration methods can adaptively suppress the noise in the highest attenuation regions . However , these methods remove not only the noise but also some edge information , which result in the reconstructed image suffers noticeable resolutions loss .
To address such two issues , a weighted ramp filter is investigated to reduce the amplification of high frequency noise , and then a total-variation based backprojection regularization ( BPR ) method is developed to mitigate the DC shift and improve the robustness to noise . The experimental results show that BPR outperforms FBP for low-dose CT imaging reconstruction .
Low-dose X-ray computed tomography ( CT ) imaging is desirable for various clinical applications due to the growing concerns about excessive radiation exposure to the patients . One strategy to achieve low-dose CT imaging is to lower the number of projection views per rotation during data acquisition .
When excessive quantum noise is present in extremely low dose X-ray CT imaging , statistical properties of the data has to be considered to achieve a satisfactory image reconstruction . Statistical iterative reconstruction with accurate modeling of the noise , rather than a filtered back-projection ( FBP ) with low-pass filtering , is one way to deal with the problem .
In the past , this has been considered as the fundamental resolution limit in many imaging problems . However , with the recent theory of compressive sampling (CS),1–3 it is now possible to overcome the Nyquist limit for the accurate recovery of unknown sparse signals from underdetermined Fourier measurements in various imaging applications , e.g. , accelerated magnetic resonance imaging ( MRI ) , low-dose xray computed tomography , and optics . To facilitate more robust reconstruction , recovery algorithms usually take the form of a penalized least squares or a constrained optimization framework ( which are dependent on the specific signal representation and discretization scheme , rather than the nature of the original problem in the continuous domain ) .
While the shape energy draws the level set function towards a range of possible prior shapes , the edge-mounted Willmore energy captures the localized geometry information and smooths the surface during the level set evolution . Experimental results on segmentation of spinal vertebrae from CT images demonstrate the powerful combination of prior knowledge and geometrical flow .
Precise automatic vertebra segmentation in computed tomography ( CT ) images is important for the quantitative analysis of vertebrae-related diseases but remains a challenging task due to high variation in spinal anatomy among patients . In this paper , we propose a deep learning approach for automatic CT vertebra segmentation named patch-based deep belief networks ( PaDBNs ) . Our proposed PaDBN model automatically selects the features from image patches and then measures the differences between classes and investigates performance .
However , automated detection and segmentation are challenging tasks due to a relatively high degree of anatomical complexity , presence of unclear boundaries and articulation of vertebrae with each other . In this paper , we describe a sparse representation error minimization ( SEM ) framework for joint detection and segmentation of vertebral bodies in CT images . By minimizing the sparse representation error of sampled intensity values , we are able to recover the oriented bounding box ( OBB ) and segmentation binary mask for each vertebral body in the CT image .
The localization and identification of vertebrae in spinal CT images plays an important role in many clinical applications , such as spinal disease diagnosis , surgery planning , and post-surgery assessment . However , automatic vertebrae localization presents numerous challenges due to partial visibility , appearance similarity of different vertebrae , varying data quality , and the presence of pathologies .
PurposeIn the last decades , the increasing medical interest in magnetic resonance imaging ( MRI ) of the spine gave rise to a growing number of publications on computerized methods for spine analysis , covering goals such as localization and segmentation of vertebrae and intervertebral discs as well as the extraction and segmentation of the spinal canal and cord . We provide a critical systematic review to work in the field , putting focus on approaches that can be applied to different imaging sequences and settings .
In this paper , we proposed a mathod for lumbar vertebra CT image segmentation based on the contourlet transform and artificial neural networks(ANNs ) . The proposed method consists of three portions .
Precise detection and segmentation of spinal vertebrae are crucial in the study of spinal related disease or disorders such as vertebral fractures . Identifying severity of fractures and understanding its causes will help physicians determine the most effective pharmacological treatments and clinical management strategies for spinal disorders .
Our proposed approach has been tested on 115 vertebrae CT volumes of various patients . Quantitative comparisons validate that our proposed AGLSA is more accurate in segmenting lumbar vertebrae CT images with irregular boundaries and more robust to various levels of salt-and-pepper noise .
In most cases with medical imaging , classical inverse Radon transforms , such as an inverse Fourier transform for MRI , work well for recovering clean images from the measured data . Unfortunately in the case of X-Ray CT , where undersampled data is very common , more than this is needed to resolve faithful and usable images . In this paper , we explore the history of classical methods for solving the inverse problem for X-Ray CT , followed by an analysis of the state of the art methods that utilize supervised deep learning .
Such topics are usually not addressed across the different imaging modalities in one book , and this is a notable strength of the treatment given here . Table of Contents : Introduction / Diagnostic X-Ray Imaging / X-Ray CT / Ultrasonics / Pulse-Echo Ultrasonic Imaging / Doppler Velocimetry / An Introduction to MRI
Thus , this method can interface with low resolution imaging techniques such as Small Angle X-Ray Scattering and Cryo-Electron Microscopy . In this thesis , I show how to construct a finite element mesh from both of these sources and run simulations to replicate the results from Small Angle X-Ray Scattering and Cryo-Electron Microscopy experiments . In more detail , I have taken a structure obtained using Small Angle X-ray Scattering , ran simulations and checked that the dynamics do not affect the average X-Ray scattering curve .
Augmented reality X-Ray Vision is an emerging concept . While AR deals with virtual and real objects coexisting in the same space , AR X-Ray Vision is a subdivision of the broad spectrum of AR , which provides a " see through " vision among real world objects . In this paper , we have thoroughly analysed the existing methodologies dealing with AR X-Ray Vision and we have come up with a convenient method that enables easy implementation . This paper deals with creating a methodology to provide an X-Ray vision using the anaglyph technique and finally integrating it with the Leap Motion Controller to enable gesture interaction to move the window around through which the point of interest can be viewed .
All compound , multi-pane , and non-radiology images were eliminated using an automatic binary classifier fine-tuned with a deep convolutional neural network system . Radiology Objects in COntext ( ROCO ) dataset contains over 81k radiology images with several medical imaging modalities including Computer Tomography , Ultrasound , X-Ray , Fluoroscopy , Positron Emission Tomography , Mammography , Magnetic Resonance Imaging , Angiography . All images in ROCO have corresponding caption , keywords , Unified Medical Language Systems Concept Unique Identifiers and Semantic Type .
Python Environments Running a Python Program Basic Python Statements and Data Types Computing using Python Modules Introduction Python Modules Numpy Scipy Matplotlib Python Imaging Library Scikits Python OpenCV Module Image and Its Properties Introduction Image and Its Properties Image Types Data Structures for Image Analysis Programming Paradigm Image Processing using Python Spatial Filters Introduction Filtering Edge Detection using Derivatives Image Enhancement Introduction Pixel Transformation Image Inverse Power Law Transformation Log Transformation Histogram Equalization Contrast Stretching Fourier Transform Introduction Definition of Fourier Transform Two-Dimensional Fourier Transform Convolution Filtering in Frequency Domain Segmentation Introduction Histogram-Based Segmentation Region-Based Segmentation Segmentation Algorithm for Various Modalities Morphological Operations Introduction History Dilation Erosion Grayscale Dilation and Erosion Opening and Closing Hit-or-Miss Thickening and Thinning Image Measurements Introduction Labeling Hough Transform Template Matching Image Acquisition X-Ray and Computed Tomography Introduction History X-Ray Generation Material Properties X-Ray Detection X-Ray Imaging Modes Computed Tomography ( CT ) Hounsfield Unit ( HU ) Artifacts Magnetic Resonance Imaging Introduction Laws Governing NMR and MRI Material Properties NMR Signal Detection MRI Signal Detection or MRI Imaging MRI Construction T1 , T2 , and Proton Density Image MRI Modes or Pulse Sequence MRI Artifacts Light Microscopes Introduction Physical Principles Construction of a Wide-Field Microscope Epi-Illumination Fluorescence Microscope Confocal Microscopes Nipkow Disk Microscopes Confocal or Wide-Field ?
Cover image : X-ray images of woods ( series N0010 colored with ‘ hot ’ colormap ) . © Springer International Publishing Switzerland 2015 D. Mery , Computer Vision for X-Ray Testing , DOI 10.1007/978 - 3 - 319 - 20747 - 6_1 1
We show the maximum gap length , dmax , on square and hexagonal lattices is Thanks are due to Graham Farr , Computer Science , Monash University , for pointing us to the literature on finding minimal vectors in lattices . IS acknowledges support from the Centre for X-Ray Physics and Imaging for this project . AK is a Monash University postgraduate student in receipt of an Australian Postgraduate Award scholarship , provided through the Australian Government .
Fine alignment of x-ray optics using wavefront sensor measurements . International Workshop on X-Ray Metrology . http://antoine.wojdyla.fr/assets/posters/iwxm2018.pdf
The purpose of this paper is to identify the threats that exist in Healthcare Information Systems ( HIS ) . The study has been carried out in three different departments namely , Information Technology Department ( ITD ) , Medical Record Department ( MRD ) and X-Ray Department in one of the leading government supported hospital in Malaysia . The hospital was equipped with Total Hospital Information System ( THIS ) environment .
For example , when dealing with brain Magnetic Resonance ( MR ) images as a biometric tool , no one can modify deliberately features of his own brain even if these features can change over the time in case of pathological individuals , or during the growth period ( not considered in this study ) . In particular , a special interest is given to brain MRI and X-Ray based biometrics . Some examples using brain MRI images , Hand and Lung X-Ray are highlighted in this paper .
Objective:3D(Three-dimensional ) lung segmentation is an indispensable step in pulmonary nodules computer-aided detection based CT images . In this paper , we proposed a simple and effective 3D segmentation method , which combined the threshold , connected regional mark and morphological techniques .
Lung segmentation is an indispensable step in pulmonary nodules computer-aided detection based CT images . Aimed at decreasing the Snake dependence on initial contour points , improving the accuracy of segmenting the depth sag area and resistance of noise , this paper proposes an interactive lung parenchyma segmentation algorithm , which uses improved Live-Wire algorithm to define Snake initial contour .
Finally , the final segmentation result is obtained by setting the threshold based on this paper method . After experimental comparison , this paper demonstrates the feasibility and effectiveness of convolution neural network in the segmentation of pulmonary CT images .
With the ability to display anatomy free from superimposing structures and greater visual clarity , Multi-Detector-CT has shown to be more sensitive than the chest radiograph in identifying alterations of lung parenchyma . In automated evaluation of pulmonary CT-scans , quantitative image processing techniques are applied for objective evaluation of the data . A number of methods have been proposed in the past , most of which utilize simple densitometric tissue features based on the mean X-ray attenuation coefficients expressed in terms of Hounsfield Units [ HU ] .
In this paper , in order to overcome this difficulty , the deep CNNs GoogleNet and ResNet are pre-trained on the large scale database ImageNet . The fully connected layers and the classifiers of the pre-trained networks are replaced to complete the classification of CT images of pulmonary nodules . A sub set of the Lung Image Database Consortium image collection ( LIDC-IDRI ) is used to fine-tune the network and validate the classification accuracy .
Computer-aided diagnosis ( CAD ) has already been widely used in medical image processing . We recently make another trial to implement convolutional neural network ( CNN ) on the classification of pulmonary nodules of thoracic CT images . The biggest challenge in medical image classification with the help of CNN is the difficulty of acquiring enough samples , and overfitting is a common problem when there are not enough images for training .
We implement the 10-folder cross validation ( CV ) to testify the robustness of the classification model we trained . The result demonstrates that the transfer learning of the LeNet-5 is good for classifying pulmonary nodules of thoracic CT images , and the average values of Top-1 accuracy are 97.041 % and 96.685 % respectively . We believe that our work is beneficial and has potential for practical diagnosis of lung nodules .
We have created a content-based image retrieval framework for computed tomography images of pulmonary nodules . When presented with a nodule image , the system retrieves images of similar nodules from a collection prepared by the Lung Image Database Consortium ( LIDC ) .
We present a novel algorithm for the registration of pulmonary CT scans . Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework .
The goal of computerised medical image analysis and interpretation is to detect abnormal appearance of the imaged anatomy and to assist radiologists in identifying and integrating all the useful information available in an image ( Brown & McNitt-Gray , 2000 ) . There is a growing number of computer-aided diagnosis ( CAD ) systems aimed at automating the analysis of lung CT images and supporting diagnosis ( Uppaluri , et al. , 1999 ; Uchiyama et al. , 2003 ; Sluimer , 2005 ; Zrimec et al. , 2007 ; Tolouee et al. , 2008 ) . Uppaluri et al. ( 1999 ) presented a CAD system for detecting six lung tissue patterns using textural features .
Detection and segmentation of fissures is useful in the clinical interpretation of CT lung images to diagnose the presence of pathologies in the human lungs . A new automated method based on marker-based watershed transformation has been proposed to segment the fissures considering its unique structure as a long connected component .
Computed Tomography ( CT ) lung nodules segmentation is a challenging task for detecting the nodules from images . Medical experts segment the medical images manually , that is almost not clear which leads to false detection .
Some evidence suggests that periodic screening tests with the CT of patients will help in reducing the mortality rate caused by the lung cancer . Acomplete and accurate computer aided diagnosis ( CAD ) system for detection of nodules in lung CT images consists of three main steps : extraction of lung parenchyma , candidate nodule detection and false positive reduction . While precise segmentation of lung region speed upthe detection process of pulmonary nodules by limiting the search area , in candidate nodule detection step we attempt to include all nodule like structures .
In this study , we developed a novel system , called Gaze2Segment , integrating biological and computer vision techniques to support radiologists ’ reading experience with an automatic image segmentation task . During diagnostic assessment of lung CT scans , the radiologists ’ gaze information were used to create a visual attention map . Next , this map was combined with a computer-derived saliency map , extracted from the gray-scale CT images .
Image registration of lung CT images plays a crucial role in estimating lung motion that is of great significance in image guided radiation therapy . However , the respiratory-induced deformation makes registration of lung CT images rather difficult and challenging .
This paper proposes a novel method for image registration of lung CT scans . Our approach consists of a procedure for automatically establishing landmark correspondences in lung CT scan pairs and an elaborate variational image registration scheme .
The proposed end-to-end DNN demonstrated better sensitivity and accuracy for the task compared to a two-step approach , in which the reconstruction and detection DNNs were trained separately . A significant reduction of false positive rate on suspicious lesions were observed , which is crucial for the known over-diagnosis in low-dose lung CT imaging . The images reconstructed by the proposed end-to-end network also presented enhanced details in the region of interest .
The aim of the proposed work is to improve low-dose lung CT ( LDCT ) screening using the processing of weighted intensity averaging over large-scale neighborhoods ( WIA-LN ) . Both current and voltage reductions were considered for LDCT imaging .
This paper presents a joint spatial-intensity-shape ( JSIS ) feature-based method for the segmentation of CT lung nodules . First , a volumetric shape index ( SI ) feature based on the second-order partial derivatives of the CT image is calculated .
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation. RIANet: Recurrent interleaved attention network for cardiac MRI segmentation Improved Shape Modeling of Tubular Objects Using Cylindrical Parameterization Enlargement, subdivision and individualization of statistical shape models: Application to 3d medical image segmentation Improved total-variation noise-reduction technique with gradient method using iteration counter and its application in medical diagnostic chest and abdominal X-ray imaging Bio-inspired methods modeled for respiratory disease detection from medical images Neural-networks-based Photon-Counting Data Correction: Pulse Pileup Effect. Noise and spatial resolution characteristics of unregularized statistical iterative reconstruction: an experimental phantom study Pre-filter that incorporates the noise model Algorithm-based low-dose computed tomography image reconstruction Investigation on scale-based neighborhoods in MRFs for statistical iterative reconstruction A variational framework for low-dose sinogram restoration Low-dose X-ray Computed Tomography Image Reconstruction Using Edge Sparsity Regularization Image fusion for low-dose computed tomography reconstruction Backprojection regularization with weighted ramp filter for tomographic reconstruction Adaptive nonlocal means-regularized iterative image reconstruction for sparse-view CT Nonlinear sinogram smoothing for low-dose X-ray CT Low-rank Fourier interpolation for compressed sensing imaging A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method Automatic Deep Feature Learning via Patch-Based Deep Belief Network for Vertebrae Segmentation in CT Images Joint detection and segmentation of vertebral bodies in CT images by sparse representation error minimization Automatic vertebrae localization from CT scans using volumetric descriptors On computerized methods for spine analysis in MRI: a systematic review An Approach to Lumbar Vertebra CT Image Segmentation Using Contourlet Transform and ANNs Identification of spinal vertebrae using mathematical morphology and level set method Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation Applications of Deep Learning for Ill-Posed Inverse Problems Within Optical Tomography Introductory Medical Imaging A stochastic finite element model for the dynamics of globular proteins Augmented Reality X-Ray Vision with Gesture Interaction Radiology Objects in COntext (ROCO): A Multimodal Image Dataset Image Processing and Acquisition Using Python Computer Vision for X-Ray Testing Farey Sequences and Discrete Radon Transform Projection Angles Leveraging raytracing softwares in diffraction-limited beamline design and simulation (Conference Presentation) Threats to Health Information Security Beyond classical biometrics: When using hidden biometrics to identify individuals The Three-dimensional Lung Segmentation from CT Images Lung segmentation based on improved Snake model Segmentation of Pulmonary CT Image by Using Convolutional Neural Network Based on Membership Function Automated characterization of normal and pathologic lung tissue by topological texture analysis of multidetector CT A Transfer Learning Method for CT Image Classification of Pulmonary Nodules Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning BRISC—An Open Source Pulmonary Nodule Image Retrieval Framework Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration Computer-aided Analysis and Interpretation of HRCT Images of the Lung A Novel Approach to Detect Fissures in Lung CT Images using Marker-based Watershed Transformation Segmentation of CT lung nodules using FCM with firefly search algorithm Detection of Pulmonary Nodules in CT Images Using Template Matching and Neural Classifier Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation Registration of Lung CT Images Using B-spline Based Free-Form Deformation Combining Automatic Landmark Detection and Variational Methods for Lung CT Registration End-to-End Abnormality Detection in Medical Imaging Low-dose lung CT processing using weighted intensity averaging over large-scale neighborhoods Shape-based ct lung nodule segmentation using five-dimensional mean shift clustering and MEM with shape information
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MIAR BHI ISICDM EMBC ISBI Medical Imaging: Image Processing EMBC CVII-STENT/LABELS@MICCAI Computer Vision for X-Ray Testing IAS EUVIP CSE/EUC (1) Medical Imaging: Computer-Aided Diagnosis WISATS (2) MCV/BAMBI@MICCAI ISBI
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