access to medical care is sometimes very difficult to be reached from people living in rural and underserved areas .
the implementation of systems support different telehealth technologies as well as on evidence - based medicine .
a fundamental change in the management and delivery of healthcare , by leveraging telehealth applications and solutions , is crucial to get the most cost - effective and efficient use out of available healthcare resources .
it is therefore crucial to develop a general information exchanged e - medical system to enable its users to perform online and offline medical consultations .
this means by consultation of additional medical experts , medical diagnosis can be stated more precisely , and certified . during the medical diagnosis , image analysis techniques combined with doctor 's opinions could be useful for final medical decisions .
telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology , where participants are separated by geographic , time , social and cultural barriers .
it represents a wide range of variables including clinical application , characteristics of the information being transmitted , temporal relationships of data transfer , and the organizational context . in quality assessment studies on information systems effectiveness , data quality ,
the iso and standards and issues related to the quality of medical care exist already . over the past years
simulation refers to the artificial generation , representation , and interaction of a complex - real - world process . in the medical area
the trainee has the ability to realistically learn a number of skills without the physical presence of a real patient .
the benefits are availability and evaluation of different medical cases ; clinical human performance and mastering complex or even critical situations offers a safe study environment in absence of the physical patient , especially in cases where the distance is a crucial parameter for the diagnosis and treatment . because there is a close relation between telemedicine and medical imaging , it is essential to develop e - medical systems appropriate to provide information for the anatomical structure of the human body . moreover the online - mode gives the opportunity to communicate over far distances with a given partner in real time . in this case
both partners look at the same image material and through text messages ( chat window ) and transfer mouse actions to the remote pc , where they can discuss interactively over a medical case . methods for providing that kind of information are based on segmentation techniques .
methods for performing segmentations vary widely depending on the specific application , imaging modality , and other factors like treatment ( e.g. , cancer treatment , image - guided surgery , and invasive techniques ) or diagnostic imaging [ 24 ] . in this work we will present segmentation techniques that improve the time and interaction one needs to define one or more structures .
this methodology involves interpolation methods for the volume definition ( manual and semiautomatic ) and methods for the semiautomatic organ shape extraction .
once an accurate segmentation is obtained , this information may be used by the doctors to compare the volume and morphology characteristics of each region against known anatomical norms , other regions in the same image set , and corresponding regions in related image sets .
telemedicine , and recently e - health , are the names of telecommunication tools , which are able to improve the equitable access to healthcare .
the ability of telemedicine to facilitate medical care , irrespective of distance and availability of medical specialists on site , makes it attractive to the healthcare sector .
telemedicine promises to enhance continued on - the - job medical training of doctors , nurses , and other healthcare practitioners in rural areas , and this perspective is extremely useful for all developing countries .
telemedicine is defined as the practice of medicine at a distance , where a messenger carrying over a medical advice from a distant expert to the bed of a patient .
the physicians and health care organizations are therefore able to guarantee high - quality care , without the patient having to travel and being carried over long distances .
effectiveness of the telemedical systems could be seen especially in demanding diagnostics ( cancer disease ) where distance is an important factor in patient treatment ( radiotherapy ) .
radiotherapy is one of the most effective forms of cancer treatment , undergone by roughly a third of all cancer patients .
once cancer has been diagnosed , patients generally have three fundamental treatment alternatives : surgery , radiotherapy , or chemotherapy .
none of these options is particularly comfortable , and they each have positive and negative aspects .
radiotherapy is the only choice in certain cases , such as fast - growing tumors , in the postoperative treatment of breast or lung cancer , some prostate types and many others , where radiotherapy is often used to prevent the regrowth of malignant tissue .
the scope is on the one hand the virtualization of the complete treatment planning procedure ( to deal with the digital patient data rather than the real patient ) , on the other hand to make the virtual patient available on different sites than those of its physical location and therefore allow telematics cooperation of radiooncology centers .
the first fact will mainly increase efficiency of personnel and accuracy ( quality ) of treatment , while the second will mainly reduce costs by allowing virtual sharing of extremely expensive devices as well as remote expert assistance / service . although system demonstration will be based on a worldwide commercially available radiotherapy planning system which will extend its efficiency by adding telematics components , mainly based on generally available networks ( internet , isdn , phone lines ) .
the main efforts will be entirely focused on developing the innovative telematics tasks and implementing these on existing professional and commercially available technology .
due to the availability of telematics components expensive devices such as ct ( computed tomography ) scanners will become virtually available to hospitals without such devices physically present .
in addition , remote experts can generate the treatment plan of a patient and/or validate plans of their colleagues on - line ( consultation conference ) or off - line ( desktop system ) .
powell iii   states that telemedicine programs can help patients in remote areas being cared at their duty station , when they might otherwise have traveled for care . as a result
there is an increase in appropriateness of consultation request , improved access to care , enhanced provider education , and higher customer satisfaction .
to generate views from different anatomical structures contained in ct data sets or to perform treatment planning techniques , it is essential ( 1 ) to extract the corresponding data from the image and ( 2 ) to accurately define the target volumes as well as the organs at risk for the treatment outcome ; both methods can be done via segmentation process .
the first fact will mainly increase efficiency of personnel and accuracy ( quality ) of treatment , while the second will mainly reduce costs by allowing virtual sharing of extremely expensive devices as well as remote expert assistance / service .
the main efforts will be entirely focused on developing the innovative telematics tasks and implementing these on existing professional and commercially available technology . in order to make possible the data transference ,
aim of the platform is the collaboration between doctors and/or citizens located in countries with different health care systems , different technology infrastructure , and different needs , in order to enhance the quality of health care services and the equal access to the health care systems . through the platform
will be possible the efficient handling of patients with chronic diseases , the early response to emergency incidents , and the reduction of human lives losses . based on the system platform users are able to perform online and offline medical consultations .
in addition to the given possibilities telemedical systems offer the following features : ( 1 ) sending and receiving of multimedia messages with text , image material , and audiomessages , over internet , isdn and analog phone line in offline and online mode .
( 2 ) graphical and textual annotations and pictograms can be added to the images .
( 3 ) in the online mode interactive communication via usage of a chat window , mouse , exchanged images , and a consolidated screen view is possible .
support of the operation in a telemedicine center and consultation clients through a client - server database all image of the patient and message - information will be stored constantly .
the principle of the online teleconsultations is to bring two geographically distanced doctors together in an online session . during that session
, both doctors will have the same view on the images and can communicate through additional communication features like chat , annotations , and so forth . in that way , expert knowledge and advice is provided to doctors in rural and crisis situation areas , or simply to any doctor in the field , who needs help in diagnosing a clinical case and do not want or is not able to send the patient to the expert physically ( figure 1 ) .
extended examples of telemedical systems are described and illustrated in [ 6 , 7 ] .
these systems have the ability to generate 2d and 3d images using only the original ct data of the patient .
multiplanar reconstructions ( mpr ) can be generated in real time in the orthogonal , coronal , and sagittal directions , and any oblique direction .
the 3d reconstructed images are a must in ct - sim systems in order to simulate the patient anatomy and the images generated from the real simulator or the treatment machine .
the first layout contains four windows and the displayed images are the beam 's eye view ( bev ) , the observer 's eye view ( oev ) and the room view ( rm ) , together with the axial slices .
the second layout is composed again of four windows , containing the three orthogonal slice directions and the oev .
this layout is ideal for navigation through the ct volume , for volume delineation and to observe complex radiation field arrangements .
this layout has six windows emphasizing the size of the bev image , since the physicians feel comfortable working with this image . in both rendering views , oev and bev
the important rtp imaging is the dynamic x - ray image on the simulator monitor , which is generated from the viewpoint of the beam source , called beam 's eye view ( bev ) image , see the bev view direction in figure 5 . in the system the corresponding window , called the bev window , displays the interactive drr images . through the bev window
, the physicians can investigate the patient anatomy in the drr image as they observe it on the simulator monitor .
observer 's eye view ( oev ) window visualizes the patient from the room 's eye view , which is the same view as the physicians observe the patient in the simulator room , see the oev view direction in figure 5 .
as we explained before , all of the images in both bev and oev windows are generated with the patient ct scan by the direct volume rendering ( figure 2 ) .
the system can use large amounts of data , from 40 up to 300 slices , in order to produce high - quality anatomical images .
it enables reading of ct data , acquired not only with a single slice ct scanner ( such as a siemens somatom plus 4 ) , but also multislice data ranges , acquired in this study with four slices ( somatom volume zoom ) .
it can be connected via network directly to any ct scanner that supports dicom protocol .
the system allows the adaptation to any linac configuration through a machine configuration module that is designed according to the international standard for radiotherapy equipment ( iec 1217 ) .
this configuration involves limitation on movements of the mechanical component and description of the component 's dimensions ( e.g. , multileaf collimator , or mlc ) . instead of the presence of the physical patient 's body ,
a virtual patient 's body as well as the vs machine is displayed , which is called the simulator window . through the simulator window
, the physicians can investigate the movements of the machine as they observe it through the lead glass in their simulator operation room ; see the sim view direction in figure 3 .
the effect of the radiotherapy treatment is based on the precise delivery of high irradiation dose on the tumor site without damaging the surrounding healthy tissues .
therefore patient positioning , target volume definition , and irradiation field placement are very critical steps while planning the irradiation process
. one of the significant technological advances in radiotherapy in the past 20 years is the implementation of ct or virtual simulators ( vs ) , in the clinical routine .
first proposed the concept , often termed ct - sim to distinguish it from sim - ct where a simulator is modified for ct use and by the late 1990s several designs and clinical assessments of ct virtual simulators have been reported [ 815 ] . using vs ,
the clinical routine is modified accordingly ( figure 4 ) [ 16 , 17 ] .  
transfer ct data to vs. the physician defines the tumour volume and the organs at risk and she / he will place the necessary fields relative to the tumour volume .
the simulation plan and the ct data are transferred via dicom ( digital image and communication in medicine ) server to the tps for dose calculation and final treatment plan optimization . verify patient position on linac before irradiation .
perform treatment on the treatment machine ( linear accelerator or linac ) . during the images ' transferring in teleradiology important factor for effective diagnosis
volume rendering is the technique according to which a scalar field of data with discrete values , volume data , is selectively sampled in order to generation a useful image in relation to the sampled values .
a volume data set is typically a set of discrete samples of one or more physical properties in a limited object space .
a volume data set is typically a set of discrete samples of one or more physical properties in a limited object space , v(x ) , x  rn , in which { x } is a set of sampling points ; n is the dimension of the sampling space , usually n = 3 , that is , 3d volume data ; v represents the sampling values , it can be a single - valued or multivalued function . according to the distribution of sampling points ( i.e.
, the structure of x ) , volume data sets are classified into structured and unstructured data sets . in medical imaging , volume data is usually a structured data set , typically organised as a stack of slices ; v can be single valued ( e.g. , the ct hounsfield value ) or multivalued ( e.g. , density , t1 , t2 in mri ) .
the resulting data structure is an orthogonal 3d array of voxels , each representing a sampling value .
the rendering methods which are proposed in this work are based on the work of .
( 1 ) transparent mode : digitally reconstructed radiographies ( drr ) with general energy exponential equation : 
 ( 1)i(s)=i0exp(dk(t)dt ) , 
 where k is the attenuation coefficient and  is the wavelength in distance d ; ( 2 ) surface reconstruction mode : isovalue , gradient   and semitransparent with general energy form : 
 ( 2)i(s)=dc(t)(t)t(t)dt , 
 where c(t ) is the shading intensity at point t ,  is the opacity , and t(t ) is the accumulated transparency from 0 to point t in distance d. drr images generated from ct digital volumes are often called using the term digitally reconstructed radiographs ( drrs ) ( figure 5 ) .   to reconstruct a drr
each voxel acquired using ct has a value that is called the hounsfield unit upon the name of the father ct .
the hu can be estimated using the following formula ( 3 ) : 
 ( 3)hu=(water)water1000 , 
 where  , water are the attenuation coefficient of the material for the specific voxel and the water , respectively .
when on the above equation one replaces the m = water , then will receive a huwater = 0 .
the hounsfield units have no upper limit but usually for medical scanners a range between 1024 to + 3071 is provided . apparently 4096 different hu values are provided and therefore to illustrate the complete range of the hu on a volume 12-bit voxels are required .
probably the most common methods for reconstructing surfaces directly from voxels are the gradient surface and isosurface model .
this rendering model is widely used in almost all medical data sets to render the surface detected by the gradient operator .
levoy   presented this concept , which is effectively used in most medical imaging applications for the last decade .
depending on the data set size and on the size of the resulting 3d image , which influence the number of rays , a 3d view image can be calculated almost in real time ( 0.8  sec to 2.3  sec ) . before applying any visualization routine the beam and block polygon
are rotated and translated so as to achieve the appropriate orientation relative to the ct data .
the common visualization of beam , block polygons , and the ct data is achieved using a 3d scan - line conversion algorithm .
for the 3d visualization of the polygon , the depth value of the point plane is compared with the depth information of the surface z - buffer that is calculated by the volume - rendering pipeline .
if the depth value of the point plane is closer to the spectator than the calculated surface position , then the original pixel value is mixed with the point plane color value ( figure 6 ) . if zv(i , j ) is the function during the volume rendering process .
if one compares the current edge z - depth zp(i , j ) with the value of the image z - buffer then could make the following two assumptions .
if zp(i , j ) < zv(i , j ) then the current polygon edge is behind the volume and is hidden from the viewer . in this case
no value for the light mask is accumulated . if zp(i , j ) > zv(i , j ) then the current polygon edge is in the front of the volume and is visible from the viewer . in this case
accurate segmentation is required for volume determination , 3d rendering , radiation therapy , and surgery planning .
traditionally , image segmentation denotes the technique of extraction of image entities or structures ( regions or objects ) , so that the outlines of these structures will coincide as accurately as possible with the physical 2d object outlines [ 22 , 23 ] .
an object region is a set of image pixels , which are similar with respect to a homogeneity criterion such as texture .
a problem that occurs is that images either over - estimated , with objects divided into parts or images are incorrectly segmented , with or without more objects segmented as one object .
we will present an effective semiautomatic method , based on the boundary tracking technique , which improves the time when one or more structures are in use .
the implemented algorithms can segment within a few seconds the complete volume of specific organs , for example , lungs , skin , and spine .
the only interaction of the user is to select the starting point in the region of interest and the algorithm will track the object boundaries in 3 dimensions . the segmentation process might involve complicate structures and in this case usually only an expert can perform the task of the identification manually on a slice - by - slice base .
humans can perform this task using complex analysis of shape , intensity , position , texture , and proximity to surrounding structures .
of course , all these features are differently qualified depending on the experience of the user . to perform a similar procedure automatically using a computer since today
in other cases where simpler anatomical regions with a very distinguishable shape must be identified an algorithm can perform this task .
currently there are only dedicated algorithms for different regions of the human body . to generate a
 complete ,  with a general meaning of the term , segmentation application numerous tools and algorithms must be combined .
image segmentation approaches may be performed in one of three ways : ( 1 ) completely manual : an operator with proper training draws the required boundaries manually , using a mouse or other pointing devices .
this requires expert knowledge of the operator and skills in drawing boundaries with available tools .
it will pose a serious problem when the size of data sets grows , ( 2 ) fully automatic : the current available techniques , such as thresholding or region growing are not sophisticated and robust enough for our application .
the use of these techniques is therefore restricted to simple boundaries and requires high image quality , and ( 3 ) semiautomated : this method uses boundary finding results generated by sophisticated automatic algorithms as initial guess , and then allows an operator to edit the required boundaries manually .
this method still requires expert knowledge of the operator , but reduces the time and effort needed .
the differences in these types of interaction are the amount of time and effort required , as well as the amount of training required by an operator .
even automated segmentation methods typically require some interaction for specifying initial parameters that can significantly affect performance . 

linear interpolation is one of the simplest methods where linear interpolation between contours is the first approach used to provide an acceleration tool for the manual contouring .
the mechanism of the linear interpolation is applied when between the key contours at least one slice exists . to perform the linear interpolation
we create triangles between the contour points of the key contours as described in sakas . for this operation
the interpolated contour points are created after calculating the intersection of each triangle side with the intermediate slice ( figure 7 ) .
after the definition of the organ boundaries for a given 2d shape , a surface reconstruction of the 3d data defining anatomical structures must be defined .  
linear interpolation between key slices is a very common approach used in rtp process to accelerate target volume definition . to overcome the problem with local discontinuities when
one contour every time is considered , a linear interpolation technique based on surface rendering is applied using information from the triangulation of the 3d shape .
surface rendering is a process in which apparent surfaces are produced from the data volume and an image of the extracted surfaces is suitably visualized .
surface contours are modelled with bidimensional primitives , as triangles or polygons , in order to represent a 3d object .
the basic idea is to extract a surface from the 3d data volume as a collection of adjacent polygons and to visualize the extracted surface by appropriate algorithms .
the triangulation approach is described as in figure 7 : contours defined on two adjacent slices are divided into the same number of equiangular sectors .
the points corresponding to sector neighbours are connected by obtaining a series of triangles that will define the surface .
the marching - cube algorithm works on a 3d mask that defines the object of interest obtained by a threshold - based or a more complex segmentation algorithm ( figure 8) .  

semiautomatic segmentations methods combine the benefit of bath manual and automatic segmentation techniques . by supplying initial information about the region of interest ,
any remaining errors introduced by automatic segmentation methods may be corrected by manual editing . in this work , a boundary tracking algorithm [ 2630 ] was implemented for the segmentation part .
boundary tracking technique ( bt ) is a segmentation method that given one point along a region 's boundary follows the boundary around the region until it returns to the original point . assuming a constant boundary
, this allows for considerable variation within the region without any effect on the segmentation .
the advantage of the approach is that no assumptions are needed to be made a priori about the boundary shape , which may vary from a straight line too much more complex shapes which are difficult to parameterize .
first let us assume that the image with regions is either binary or that regions have been labeled using a specific threshold ( white for the organ of interest , black for the background ) .
the algorithm traces the edges with detail providing high accuracy to the description of the contour shape ( figure 9(a ) ) .
image - smoothing techniques are used in image processing to reduce noise . usually in medical imaging
the noise is distributed statistically and it exists in high frequencies ; therefore , it can be stated that image smoothing filters are low - pass filters .
the drawback of applying a smoothing filter is the simultaneous reduction of useful information , mainly detail features , which also exist in high frequencies .
the basis for the filtering operation is the convolution operator , where a k  k matrix known as the kernel or mask is introduced by a general form : 
 ( 4)wij=[w11w12  w1kw21w22  w2k    wk1wk2  wkk],fij=[f11f12  f1kf21f22  f2k    fk1fk2  fkk ] , 
 where f is the original image . the convolution process is defined by g = w  f. the convolution filter with the mask can also be used to find contours inside an image ; especially in volumetric and surface processing .
it is the first step of contouring detection , where the kernel isolates the part of the image where edges appeared ( large steps , rapidly changes ) .
after segmenting the regions of interest , a 3  3 sobel edge detection operator ( figure 9(b ) ) is used to improve the appearance of the contours . in the analysis of the objects in images
it is essential that we can distinguish between the objects of interest and  the rest .
the techniques that are used to find the objects of interest are usually referred to as segmentation techniques  segmenting the foreground from background .
image thresholding is a technique for converting a grayscale or color image to a binary image based upon a threshold value .
if a pixel in the image has intensity less than the threshold value , the corresponding pixel in the resultant image is set to white . otherwise , if the pixel is greater than or equal to the threshold intensity , the resulting pixel is set to black . for that purpose ,
a low - pass filter was used based on the rectangular window or box function based on the rule :  
 ( 5)i(i , j)={1,if  i(i , j)tobject,0,otherwisebackground , 
 where i(i , j ) is image matrix and t is the appropriate threshold .
object  or  background  which , due to its dichotomous nature , can be represented as a boolean variable  1  or  0 .
while there is no universal procedure for threshold selection that is guaranteed to work on all images , there are a variety of alternatives . for this case
a histogram - derived threshold was chosen based on the image histogram combined with hounsfield unit tissue values . to overcome the problem with the increasingly high number of points , that are reconstructing the contour , a point decimation algorithm ( or sequence sampling algorithm )
was used to reduce the number of points without loss of their general presentation . if a sequence of numbers was given , s = { s1 , s2 , s3 ,  ,
si1,si , si+1 ,  , sn } , then the main idea is to construct another sequence s * , where k%-values would be removed from the original sequence . summarizing the above procedures , the pseudocode for the segmentation technique is presented in algorithm 1 .
the main drawback of the method is that it is a binary approach and hence is very sensitive to gray value variations .
if the threshold value is not selected properly then the system will fail to detect the appropriate organ shape .
most of the inaccuracies of the segmentation method require the user intervention to optimize the result . to overcome the limitation
we calculate a secondary opacity volume from the original ct data based on the well - known approach from , that is very often used to visualize surfaces from scalar volume data in volume rendering . for the lungs segmentation
, two regions would be investigated : ( a ) concave and ( b ) convex . for the convex regions
the algorithm requires an initial point to start the tracing of the edge of the object under investigation .
the initial point travels to the vertical or horizontal direction until the edge of the investigated object is reached .
then the algorithm will start to exam the surrounding pixel of that edge and check whether they belong to the current edge or not .
for the concave regions a different approach was achieved implementing a two moving position methods based on where the starting point appears : ( i ) outside point and ( ii ) inside point .
the algorithm uses a constant threshold selection with levels 20 to 70 hounsfield values . 

outside pointconsidering the case where the object is concave and a starting point was given , extended experiments indicate false segmentation alarms , when the starting point remains at the same position for all the slices ; to overcome this , a region growing technique ( grey color ) was applied to find the first white region inside the organ ; a region growing approach ( green color ) was used , to avoid undesired results in case the point position was moved at the edge of the region . 
  considering the case where the object is concave and a starting point
was given , extended experiments indicate false segmentation alarms , when the starting point remains at the same position for all the slices ; to overcome this , a region growing technique ( grey color ) was applied to find the first white region inside the organ ; a region growing approach ( green color ) was used , to avoid undesired results in case the point position was moved at the edge of the region .
the region - growing ( rg ) algorithm is used very often in the region - based segmentation techniques and has been successfully incorporated into several medical imaging applications for the extraction of organ volumes . in this work
we follow a simple design principle since the region boundaries that must be detected have low complexity and high contrast differences with the surrounding tissues .
the rg algorithm is initiated with a seed voxel selected manually , which is known to be a part of the voxel of interest , room air or skin surface .
if the difference in grey level is between the voxel under investigation and its six adjacent voxels are less than a given threshold , then the current voxel is merged with the growing region .
this process is repeated recursively to grow the object until not more voxels can be found .
the starting point changes and the boundary tracking technique continue as before ( figure 10 ) . 

inside pointconsider now the case where the starting point is inside the lung region and close to the area of high tissue ( small white spots ) . in this case using the original method , the tracking technique probably will spot ( for some slices ) the white areas avoiding the bigger lung region .
extended experimental results suggest that these regions have maximum area close to 6  cm : if the object is smaller than 6  cm then it is removed and the process continues until a larger region corresponds to the overall lung area is found . with this restriction
consider now the case where the starting point is inside the lung region and close to the area of high tissue ( small white spots ) . in this case using the original method , the tracking technique probably will spot ( for some slices ) the white areas avoiding the bigger lung region .
extended experimental results suggest that these regions have maximum area close to 6  cm : if the object is smaller than 6  cm then it is removed and the process continues until a larger region corresponds to the overall lung area is found . with this restriction
the method avoids to segment areas that are meaningless ( figure 11 ) . for visualizing volumetric medical data , sequences of 2d images
are piled up to recreate the three - dimensional structure ( figure 12(a ) ) .
usually , in this step linear interpolation of adjacent slices is needed for the generation of new slices ( figure 12(b ) ) , since one of the problems resulting from image acquisition is the space between slices .
this problem occurs because the sampling interval between slices is normally greater than the generated image resolution , and then the volume voxels are not cubic .
after interpolation this size distortion is corrected , so that the visualization algorithm could generate correct proportion projections . assuming a simple 1d example of sampling points linear interpolation offers first - degree connectivity among the sampling points , passing a straight line through every two consecutive points of the input signal . in the spatial domain , linear interpolation ( ln ) is equivalent to convolving the sampled data with the following interpolation kernel ( 6 ) : 
 ( 6)h(x)={1|x|,when  0|x|1,0,when  1|x| . 

the above interpolation kernel corresponds to a reasonably good low - pass filter in the frequency domain . in practice
we can define the linear interpolation between two points p0 , p1 in one dimension as follows ( 7 ) : 
 ( 7)px=(1x)p0+xp1px = p0+x(p1p0 ) . 
 in two dimensions the interpolation scheme will involve four data points ' p00 , p01 , p10 , and p11 .
the interpolation can be formulated using the following formula ( 8) : 
 ( 8)pxy=(1x)(1y)p00+(1x)yp01  + x(1y)p10+xyp11 . 

this interpolation method is known as bilinear interpolation and is commonly applied on image interpolation for example , when magnifying raster images . in volumetric data
linear interpolation is known as tri - linear interpolation and estimates the value of the result voxel considering the neighbourhood of eight ( 8) voxels ( p000 , p001 , p011 , p010 , p100 , p110 , p101 , and p111 ) ( figure 13 ) with estimation formula for pxyz ( 9 ) : 
 ( 9)pxyz=(1x)(1y)(1z)p000+x(1y)(1z)p100  + xy(1z)p111+(1x)y(1z)p010  + ( 1x)(1y)zp001+x(1y)zp101  + ( 1x)yzp011+xyzp111.figure 14 illustrates different 2d and 3d reconstruction examples of segmented structures . 
 figure 15 illustrates examples where volume segmentation techniques and bean object reconstruction using z - buffer methodology are applied . to assess the effectiveness of the methods and its sensitivity , the following measures are considered : ( 1 ) area of a contour in cm , ( 2 ) periphery of a contour in cm , ( 3 ) computational time in sec and ( 4 ) number of points for the contours drawing .  
table 1 illustrates the performance of the segmentation methods for various slices using the manual , the proposed method , and the proposed method after the point decimation process .
target volume and critical structure definition is a complex and time - consuming process in radiotherapy : although the complexity varies for different anatomical sites . in ct - sim and plan evaluation ,
the physicists and radiation oncologists interact closely to subjectively identify the plan most appropriate for the individual patient . in order to reduce the investment of time and effort by the radiation oncology staff ,
these systems thus allow the user to draw contours around the tumour as target [ ptv ] and around normal tissues and organs of interest on a slice - by - slice basis .
this provides at the same time , a cross - reference to planar images . organs with large differences in their radiation dose distributions can be segmented semiautomatically . in terms of user effort
the only action required from the user is the selection of a starting point for the algorithm on the original axial slices .
performance analysis of the system is taking place where the experiments are performed on a double processor ( 2xpentium iii 450  mhz ) pc with physical memory 512  mb .
for each view we performed time measurements for several rendering image sizes using different data resolution .
the goal of this experiment is to examine the volume rendering interactivity as well as to prove the effectiveness of the system for large amount of data .
a comparison for the system memory relative to different amount of data is presented in figure 17 .
important is that a pc with 512  mb physical memory can manipulate up to 300 slices with a rendering speed for transparent mode between 1.2 and 1.8 seconds and for the surface mode between 0.6 and 0.9 seconds .
accuracy of the system is very important for the system implementation especially for the investigation of ( 1 ) ct volume data orientation and ( 2 ) treatment parameters , including table translation , gantry , collimator , and table rotation , reference point placement , beam , and block size . commonly slices thickness between 3  mm to 5  mm and total number of slices maximum to 120 slices are used to implement segmentation analysis or radiotherapy treatment planning dose
. a cubic phantom of size 120  120  mm was initially used to exam the accuracy of the system . inside the phantom spheres of diameter 2  mm
the total number of produced slices was 120 , in square ct matrices of 512  512  pixels .
the accuracy of the routines automatic field and block adaptation is related to the resolution of the bev image ( figure 18 ) . in most cases
the bev image has the size of 256  256 up to 400  400 .
ct - sim is a technological advance that has already been tested in our clinical environment with demonstrable advantages particularly for complicated field arrangements .
the new , innovative virtual simulator system is a very useful additional tool for the radiation oncologist and makes the requirement for a conventional x - ray simulator redundant .
a significant advantage of the software is that it can run on portable computers such as laptops .
the system can be integrated immediately into the clinical environment via a direct interface to the ct scanner .
combination of different diagnostic ( e.g. , mr and pet ) and radiotherapy ( e.g. , mv portal detectors ) imaging modalities will make the ct - sim an even more effective tool in radiotherapy .
this technology could be combined with telemetrically techniques for the improvements of health services telemedicine applications are largely image - based .
the main purpose of this work is to represent an efficient interactive , easy - to - use medical system that provides modules for online and offline collaboration of medical doctors together with software products .
moreover new improvements for the system starts to be implemented based on telemedical applications ( this work is under development ) where an online mode gives the opportunity to communicate over far distances with a given partner in real time . in this case
both partners look at the same image material and through text messages ( chat window ) and transferred mouse actions to the remote pc , they can discuss interactively over a medical case .
the system combines low price , low weight , mobility , and a wide range of nonradiating examinations . as a result
the proposed platform reduces the disparity in the level of medical services , between the main centers and the less assisted areas , contributing to improvement of the quality of life .
 telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology , where participants are separated by geographic , time , social and cultural barriers . 
 the shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future . 
 it is therefore crucial to develop a general information exchange e - medical system to enables its users to perform online and offline medical consultations through diagnosis . during the medical diagnosis 
 , image analysis techniques combined with doctor 's opinions could be useful for final medical decisions . 
 quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest . in medical images , 
 segmentation has traditionally been done by human experts . 
 even with the aid of image processing software ( computer - assisted segmentation tools ) , manual segmentation of 2d and 3d ct images is tedious , time - consuming , and thus impractical , especially in cases where a large number of objects must be specified . 
 substantial computational and storage requirements become especially acute when object orientation and scale have to be considered . 
 therefore automated or semi - automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use . 
 the main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems . 
1. Introduction
2. Motivation
3. Telemedical Platform
4. Radiotherapy Treatment Planning (RTP)
5. Segmentation Techniques
6. Volume Definition
7. Conclusions