Multiscale Graph Cuts Based Method for Coronary Artery Segmentation in Angiograms

Abstract Context X-ray angiography is the most used tool by clinician to diagnose the majority of cardiovascular disease and deformations in coronary arteries like stenosis. In most applications involving angiograms interpretation, accurate segmentation is essential to extract the coronary artery tree and thus speed up the medical intervention. Materials and Methods In this paper, we propose a multiscale algorithm based on Graph cuts for vessel extraction. The proposed method introduces the direction information into an adapted energy functional combining the vesselness measure, the geodesic path and the edgeness measure. The direction information allows to guide the segmentation along arteries structures and promote the extraction of relevant vessels. In the multiscale analysis, we study two scales adaptation (local and global). In the local approach, the image is divided into regions and scales are selected within a range including the smallest and largest vessel diameters in each region, while the global approach computes these diameters considering the whole image. Experiments are conducted on three datasets DS1, DS2 and DS3, having different characteristics and the proposed method is compared with four other methods namely fuzzy c-means clustering (FC), hysteresis thresholding (HT), region growing (RG) and accurate quantitative coronary artery segmentation (AQCA). Results Comparing the two proposed scale adaptation, results show that they give similar precision values on DS1 and DS2 and the local adaptation improve the precision on DS3. Standard quantitative measures were used for algorithms evaluation including Dice Similarity measure (DSM), sensitivity and precision. The proposed method outperforms the four considered methods in terms of DSM and sensitivity. The precision values of the proposed method are slightly lower than the AQCA but it remains higher than the three other methods. Conclusion The proposed method in this paper allows to automatically segment coronary arteries in angiography images. A multiscale approach is adopted to introduce the direction information in a graph cuts based method in order to guide this method to better detect curvilinear structures. Quantitative evaluation of the method shows promising segmentation results compared to some segmentation methods from the state-of-the-art.

[1]  M. Hart,et al.  A method of automated coronary artery tracking in unsubtracted angiograms , 1993, Proceedings of Computers in Cardiology Conference.

[2]  Ying Sun,et al.  Back-propagation network and its configuration for blood vessel detection in angiograms , 1995, IEEE Trans. Neural Networks.

[3]  Frédéric Precioso,et al.  Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography , 2013, Medical Image Anal..

[4]  Y. Kawata,et al.  3D image analysis of the lung area using thin section CT images and its application to differential diagnosis , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[5]  Seung-Hong Hong,et al.  Adaptive tracking algorithm based on direction field using ML estimation in angiogram , 1997, TENCON '97 Brisbane - Australia. Proceedings of IEEE TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications (Cat. No.97CH36162).

[6]  Noboru Niki,et al.  An approach for detecting blood vessel diseases from cone-beam CT image , 1995, Proceedings., International Conference on Image Processing.

[7]  Frédéric Zana,et al.  Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation , 2001, IEEE Trans. Image Process..

[8]  Laurent Lecornu,et al.  Extraction of vessel contours in angiograms by simultaneous tracking of the two edges , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Milan Sonka,et al.  3D catheter path reconstruction from biplane angiograms , 1998, Medical Imaging.

[10]  S. Eiho,et al.  Detection of coronary artery tree using morphological operator , 1997, Computers in Cardiology 1997.

[11]  Jordi Vitrià,et al.  Eigensnakes for vessel segmentation in angiography , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[12]  Sergio Escalera,et al.  Accurate Coronary Centerline Extraction, Caliber Estimation, and Catheter Detection in Angiographies , 2012, IEEE Transactions on Information Technology in Biomedicine.

[13]  Nicolas Passat,et al.  3D segmentation of coronary arteries based on advanced mathematical morphology techniques , 2010, Comput. Medical Imaging Graph..

[14]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[15]  S. Pizer,et al.  Intensity ridge and widths for tubular object segmentation and description , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[16]  Maciej Orkisz,et al.  Segmentation of the pulmonary vascular trees in 3D CT images using variational region-growing , 2014 .

[17]  Vladimir Kolmogorov,et al.  What metrics can be approximated by geo-cuts, or global optimization of length/area and flux , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[18]  Kecheng Liu,et al.  A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II , 2002, IEEE Transactions on Information Technology in Biomedicine.

[19]  Christine Toumoulin,et al.  An improved graph matching algorithm for the spatio-temporal matching of a coronary artery 3D tree sequence , 2015 .

[20]  Daniel Rueckert,et al.  Automatic tracking of the aorta in cardiovascular MR images using deformable models , 1997, IEEE Transactions on Medical Imaging.

[21]  M P Chwialkowski,et al.  A method for fully automated quantitative analysis of arterial flow using flow-sensitized MR images. , 1996, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[22]  Takayuki Kitasaka,et al.  Blood vessel segmentation using line-direction vector based on Hessian analysis , 2010, Medical Imaging.

[23]  E. Sorantin,et al.  Spiral-CT-based assessment of tracheal stenoses using 3-D-skeletonization , 2002, IEEE Transactions on Medical Imaging.

[24]  Antoine Manzanera,et al.  A coronary artery segmentation method based on multiscale analysis and region growing , 2016, Comput. Medical Imaging Graph..