An Automated Method for Segmentation of Coronary Arteries in Coronary CT Imaging

Many applications in the field of medical image processing require precise estimation of diagnostic parameters of the anatomical structures to be examined. In this paper, we present a novel two-step three dimensional region statistics based active contours algorithm for automatic segmentation of the entire coronary arterial trees in 3D coronary CT images. In the first phase, we define the optimal binary labels for extracting the entire tree structure of the coronary arteries from the input images. This is accomplished by a region statistics based active contours method. The possible outliers, such as the touching vessel like structures, are removed from the segmentation in the following stage by the proposed frame by frame correction algorithm.

[1]  Panos Liatsis,et al.  A Fully Automated Framework for Segmentation and Stenosis Quantification of Coronary Arteries in 3D CTA Imaging , 2009, 2009 Second International Conference on Developments in eSystems Engineering.

[2]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[3]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[4]  Thomas Brox,et al.  Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Level Set Segmentation with Multiple Regions Level Set Segmentation with Multiple Regions , 2022 .

[5]  Don P. Giddens,et al.  AUTOMATIC SEGMENTATION OF CORONARY ARTERIES USING BAYESIAN DRIVEN IMPLICIT SURFACES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[6]  Anthony J. Yezzi,et al.  Vessel Segmentation Using a Shape Driven Flow , 2004, MICCAI.

[7]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[8]  Ross T. Whitaker,et al.  A Level-Set Approach to 3D Reconstruction from Range Data , 1998, International Journal of Computer Vision.

[9]  John W. Fisher,et al.  Nonparametric methods for image segmentation using information theory and curve evolution , 2002, Proceedings. International Conference on Image Processing.

[10]  Anthony J. Yezzi,et al.  Gradient flows and geometric active contour models , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  Chunming Li,et al.  Active contours driven by local Gaussian distribution fitting energy , 2009, Signal Process..