Research On Tracking Algorithm Of Vessel Centerline In Coronary Angiography

Cardiovascular disease has harmed human health seriously, usually manifesting as coronary artery stenosis. Coronary angiography is regarded as the leading means for the assessment of cardiovascular disease. Doctors primarily ascertain pathological locations by repetitive examinations and qualitative analysis, but far from quantification. Improving the accuracy of image analysis of coronary angiography in clinics is of significance. The development of computer processing technology enables the quantitative coronary analysis (QCA) with high speed and accuracy. In QCA analysis and clinics, vessel centerline is an extremely important geometric parameter. A method for accurate extraction of the coronary arterial centerline was presented for automated positioning of the coronary vessel borders and 3D reconstruction.The problem of automatic artery centerlines extraction in two-dimensional (2D) angiograms is tackled from different perspectives. The main work of this thesis is reported distributedly in chapters. In particular, the motivation, purpose and significance of this study are briefly introduced in ChapterⅠ. The basics of coronary angiography are described in ChapterⅡ. In ChapterⅢ, it recalls the scale space theory and defines the scale space theory can be a very good description of the image structure information. We describe the multi-scale algorithm and eigenanalysis algorithm of the Hessian matrix in the application of image processing. In Chapter IV, we report a skeleton extraction algorithm of coronary using Hessian matrix. Starting from eigenanalysis of Hessian matrix, we construct vascular eigenfunction, extract vascular region using multi-scale algorithm, perform morphological operation for connectivity among blood vessels, process thinning and get vascular centerlines. The algorithm was applied for coronary skeleton extraction by implementation using Matlab. In Chapter V are presented the vessel tracking techniques, which involve the determination of tracking direction, look-ahead distance, design of matched filtering and the size of search window. The algorithm was applied for coronary centerline extraction by implementation using Matlab. Finally, in Chapter VI are a summary and a discussion.