correct assessment , especially accurate visualization and quantification of blood vessels reflected in x - ray angiograms or angiography images , plays a significant role in a number of clinical procedures . for various medical diagnostic tasks , it is necessary to measure the vessel width , reflectivity , tortuosity , and abnormal branching . for example , detecting the occurrence of vessels of a certain width may reveal the signs of stenosis . grading of stenosis is of importance to diagnose the severity of vascular disease and to determine the treatment therapy . moreover , planning and performing neurosurgical procedures require an exact insight into blood vessels and their branches , which exhibit great variability . in planning , they provide information on where the blood is drawn and drained , to differentiate between the feeding and transgressing vessel . even as transgressing vessels need to be preserved , the feeding ones are selectively closed through the artery in interventional neuroradiology , such as the brain arteriovenous malformation treatment . during surgery the vessels serve to provide landmarks and guidelines to the lesion . in short , accuracy in the navigation and localization of clinical procedures although it is possible for medical experts to delineate vessels , manual delineation of the vasculature becomes tedious or even impossible when the number of vessels in an image is large or when a large number of images is acquired . therefore , the development of automatic and accurate vessel - tree reconstruction from angiograms is highly desirable . thick vessels have more contrast to noise ratios , as compared to small narrow ones , due to the strong presence of blood and contrast agents in the vessels [ figure 1a ] . non - uniform illumination , as shown in figure 1b , is one of the major sources of angiography image degradation , and is also a hindrance for accurate reconstruction , because it is likely to make an individual vessel break into several segments . we have divided the image processing methods used in coronary angiography into two main categories : ( 1 ) vessel enhancement ( 2 ) vessel segmentation . the current automatic computer - assisted procedures are still far from providing a precise spatial representation of the vessel tree . angiography images with typical hindrances for accurate vessel - tree reconstruction such as ( a ) low - contrast vessels and ( b ) non - uniform illumination the literature on enhancing x - ray coronary angiographic images for visualization purposes is very limited . although a number of algorithms have been proposed for angiographic image enhancement , the purpose of most algorithms is to improve the subsequent segmentation rather than visualization . these algorithms can hardly be adopted in clinical practice for improving the quality of visualization . algorithms based on specific noise models , for example , the quantum noise model , might also fail to work in practice , as image noise , that is , the undesirable appearance of mottled or grainy spots , which do not reflect the true tissue property , is a combination of various sources of noise , with different characteristics . attempting to increase the contrast of vascular structures by suppressing or removing the background structures , for example , the piecewise normalization and the rolling algorithm , is also of limited effect , as part of image noise , with the intensity value within the range of foreground structures , for example , vessels , will be enhanced as well . the step of removing the background might at the same time remove some detailed information in low - contrast angiographic images , which is very undesirable . to the best of the authors knowledge , all angiographic acquisition systems available in the market use a certain technique to enhance the acquired images in real time , that is , during the actual acquisition procedure . most of these enhancement techniques are based on the so - called unsharp masking technique and allow the operators to customize the degree of enhancement by using multiple gain levels ( typically five ) . the unprocessed image is first blurred and subtracted from the original image , creating an edge image that only contains higher spatial frequency components of the original image . this edge image is further multiplied by a certain gain level and added to the original image , resulting in an edge enhanced image . although the image edges are visually enhanced , the result is less optimal , as the image noise with a high spatial frequency will also be enhanced , which may introduce an undesirable appearance or influence the perception of the image details . then a certain warping method is applied to the mask image . after subtracting the two images , first , the mask image is decomposed into many sub - image blocks . with a template matching method , based on typical similarity measures , the rigid matching between the mask sub - image blocks and the live image can be achieved . second , the control point pairs are selected with harris corner detection , and are extracted in the original and transformed mask images . by using the multilevel b - spline as the mapping function , a global elastic registration is accomplished . many segmentation methods have been used to visualize blood vessel structures in the human body . these blood vessel segmentation methods may be classified as follows : pattern recognition , model - based tracking and propagation , neural network , fuzzy , and artificial intelligence - based methods.[1220 ] artificial intelligence - based approaches utilize knowledge to guide the segmentation process and delineate vessel structures . one knowledge source is the technique of acquisition of the properties of the image , such as cine - angiography , digital signature algorithm ( dsa ) , computed tomography ( ct ) , magnetic resonance imaging ( mri ) , and magnetic resonance angiography ( mra ) . poli and valli proposed a computationally efficient algorithm based on a set of linear filters , obtained as linear combinations of properly shifted gaussian kernels , sensitive to vessels of different orientation and radius . another type of linear filter , the morphologically connected set filter , was utilized by wilkinson and westenberg , to capture filamentous structures . together with a shape criterion that could distinguish filamentous structures from others , connected set filters similarly , eiho and qian and zana and klein used morphological operators such as erosion , dilation , and top - hat to enhance the shape of the artery and remove other points . these methods were unable to suppress sudden noise and edge noise and were less efficient in capillaries . non - linear anisotropic filtering was also applied for vessel enhancement.[2629 ] this method searched for the local orientation of a vessel to perform anisotropic smoothing without blurring its edge . while krissian et al . performed a particular version of anisotropic diffusion , orkisz et al . used a kind of filter bank called the last two references combined the outputs of directional operators , without an explicit extraction of the vessel local orientation . the main disadvantage of the methods in this category is that they can hardly detect vessels in a wide range due to the fixed scale analysis . although these algorithms can be extended to multiple scales by using sticks of variable length , the computation time would increase greatly . hessian - based multiscale filtering has been proposed in a number of vessel enhancement approaches.[3641 ] one advantage of this approach , in this category , is that vessels in a wide range of diameters can be captured due to the multiscale analysis . in this method , an input image is first convolved with the derivatives of a gaussian at multiple scales and then the hessian matrix is analyzed at each pixel in the resulting image , to determine the local shape of the structures at that pixel . the ratio between the minimum and the maximum hessian eigenvalues is small for line - like structures , but it must be high for blob - like ones . has specifically introduced several models of vessels and has used hessian eigenvalues to define a set of candidate pixels , which can be the centerlines of the vessels . for each of these candidates , pre - defined , multiscale response functions have been computed , to determine the likelihood of the pixels corresponding to vessels of different scales ( radii ) . the drawbacks of the hessian - based approaches are that they are highly sensitive to noise , due to the second - order derivatives , and they tend to suppress junctions , as junctions are characterized similar to the blob - like structures . junction suppression leads to the discontinuity of the vessel network , which is of course undesirable . to deal with this problem , agam et al . proposed a filter model that is based on the correlation matrix of the regularized gradient vectors ( first - order derivatives ) , to avoid the need for second - order derivatives . however , when dealing with angiography images , which are noisier and suffer from non - uniform illumination , it shares the same limitations of hessian - based filters in finding small and low - contrast vessels . the reason is that it is still using the hessian eigenvalues to pre - select the vessel - candidate pixels at which the filter is applied . in a new framework proposed for the vessel enhancement filter , utilizing the directional information present in an image . specifically , the input image is first decomposed by a decimation - free directional filter bank ( ddfb ) into a set of directional images , each of which contains line - like features in a narrow directional range . one is , the noise in each directional image will be significantly reduced compared to that in the original one due to its omni - directional nature . the other is , because a one - directional image contains only vessels with similar directions , this decomposition - filtering - recombination scheme also helps to preserve junctions . the directional images are recombined to generate the output image with enhanced vessels , and the suppressed directional images are recombined to generate the output image with enhanced vessels , and the suppressed applied to enhance vessels in the respective directional images . finally , the enhanced experimental results show that this approach is less noise - sensitive , can reveal the small vessel network , and avoid junction suppression . an edge detection method was also employed to extract edges that may be parallel to the opposing edges of the vessel segment . these methods use an adaptive threshold scheme to extract the vascular segments from medical images . computation of this threshold value may not be satisfactory for all the qualities of the images . ( a and e ) original images , ( b and f ) enhanced images by frangi method , 43.22 s each , ( c and g ) by shikata method , 43.11 s , and ( d and h ) by the dfb - based approach , 49.08 s. the frangi and shikata models fail to enhance small vessels accurately , but the dfb - based approach succeeds on the other hand , ridge tracing tubular objects and several other approaches were used to track the vascularity in a medical image . an adaptive tracking method was also presented to extract the extended tracts of vascularity in the x - ray angiograms . model - based approaches for explicit vessel structures were also applied to extract the vascularity in a medical image . one of these approaches used a deformable spline or the snake model to extract the vessel structures , by deforming the spline or the snake to minimize some energy functions . most of the present snake models could not provide a better capture range or evolution stop mechanism . a new external force for active contours an extension of the gradient vector flow snake ( gvf snake ) method is presented . first , adaptive balloon force has been developed to increase both the gvf snake 's capture range and convergence speed . subsequently , a dynamic gvf force is introduced to provide an efficient evolution - stop mechanism [ figure 4 ] . ( a ) input image , ( b ) improved gvf method artificial intelligence  based approaches use a rule- and knowledge - based expert system , to segment coronary vessels from digital subtracted angiograms . automated systems and high processing throughput are needed in computationally intensive tasks , including visualization of the coronary blood flow and three - dimensional reconstruction of the vascular structure from biplane medical images.[1214161820 ] the previous blood vessel segmentation algorithms are limited by at least one of the following drawbacks . the drawbacks are , applicability of the method for a limited number of morphologies , the need of user involvement to select the region of interest , and lack of adaptive capabilities , which results in poor quality of the segmentation , requiring a large computational effort for blood vessel segmentation . a model - based segmentation method is introduced for extracting blood vessel structures from poor quality coronary angiograms . this method extracts blood vessels in the angiograms by exploiting the spatial coherence existing in the image . ( a ) a real image and ( b ) its segmentation result table 1 presents the capabilities of some important algorithms mentioned earlier for coronary vessel segmentation . coronary x - ray angiography is taken as the  gold standard for the assessment of coronary artery disease . a brief comparison of the coronary vessel segmentation algorithms in the x - ray angiogram thus , algorithms presented in table 1 are specifically used for x - ray angiography . in this table , the comparison is based on different aspects . for instance as long as preprocessing is concerned , algorithms presented in the second and the third row need preprocessing . by comparing the results of these algorithms , the truc can be seen by using multiscale analysis , together with directional filters , and an acceptable performance is obtained in the segmentation of coronary vessels . in this article we have tried to cover both early and recent literature related to coronary vessel segmentation algorithms and techniques . our aim was to introduce the current segmentation techniques . accuracy of the segmentation process is crucial according to the nature of the work and leads to more precise and repeatable radiological diagnostic systems . accuracy can be improved by incorporating a priori information on vessel anatomy and making use of a high level knowledge guide for the segmentation algorithm . even though expert knowledge and guidance is essential in segmentation systems , the sheer volume of the medical image data requires more automatic segmentation systems , to reduce the work load . degree in electrical engineering from the isfahan university of technology , isfahan , iran , in 2001 . degree in communication engineering in 2004 , from the isfahan university of technology , isfahan , iran , where she is currently a phd student . saeed sadri , associate professor in electrical and computers engineering department , isfahan university of technology , isfahan , iran . degree in electrical engineering from the university of tehran , faculty of engineering , tehran , iran , respectively in 1977 and 1979 . he received the phd degree in communication engineering from the isfahan university of technology , in 1997 . alimohamad doosthoseini , associate professor in electrical and computers engineering department , isfahan university of technology , isfahan , iran . degree in communication engineering from the shiraz university , shiraz , iran , in 1975 . degree in communication engineering from the university of california , berkeley , california , usa , in 1977 . he received the phd degree in communication engineering from the stanford university , california , usa , in 1983 .
coronary heart disease has been one of the main threats to human health . 
 coronary angiography is taken as the gold standard ; for the assessment of coronary artery disease . 
 however , sometimes , the images are difficult to visually interpret because of the crossing and overlapping of vessels in the angiogram . 
 vessel extraction from x - ray angiograms has been a challenging problem for several years . 
 there are several problems in the extraction of vessels , including : weak contrast between the coronary arteries and the background , unknown and easily deformable shape of the vessel tree , and strong overlapping shadows of the bones . in this article 
 we investigate the coronary vessel extraction and enhancement techniques , and present capabilities of the most important algorithms concerning coronary vessel segmentation .