The Segmentation Of Coronary Angiogram Images

The segmentation of coronary angiogram images is the key to do visualization and quantization to complicated vessel data sets, and accurate segmentation result not only can locate the focus position accurately to assist doctors to diagnose and treat coronary heart disease, but also is the basis to rebuild three-dimensional coronary artery. As the topological structure of coronary artery is complex and the distribution of contrast medium is non-uniform, which causes the angiogram image having problems as indistinction and low contrast ratio inevitably, and creates huge difficulty to segmentation. By analyzing the eigenvalues of Hessian matrix with blob-like, tubular and plate-like structure under a certain dimension, and creating vesselness function, the vessel in two-dimensional and three-dimensional images can be strengthened in order to be convenient for the segmentation of next step. This thesis takes coronary angiogram image as the study object, analyzes and improves the methods of coronary artery segmentation and skeleton extraction which based on Hessian matrix, and the main study contents include:(1) Pretreatment enhancement and segmentation of coronary arteriogram images. Improve the imperfection of vessel enhancement arithmetic based on Hessian matrix, which includes that parameters in vesselness function are difficult to set, vascular structure is extracted incompletely, and especially the tiny structures on the end of vessel have many deficiencies and breakages. This thesis firstly utilizes the relation of Hessian matrix eigenvalues corresponding to tubular objects, creates a novel vesselness function to strengthen coronary artery, and leaves out the parameters setting. Then combines the characteristics of maximum variance between clusters, which are rapid, efficiency and powerful anti-noise capacity, proceeds thresholding to coronary artery image strengthened, and gains the segmentation result. The experiment result shows that the coronary arterial tree structures can be extracted in this way, and the arithmetic is more robust.(2) Extracting of coronary artery skeleton. Aim at the problems that the existing skeleton extraction arithmetic is generally sensitive to outline noise, and easy to generate superfluous skeleton branches, this thesis bases on segmentation result, utilizes axis transfer principle to define coronary artery skeleton, and combines Hessian matrix eigenvalues to confirm the normal direction of coronary vessels, gains the initial pixel point set of coronary artery skeleton by solving the extreme point on the normal direction, then takes this initial point set as the input, traverses to search the dot pair which is satisfied with connectedness code, finally extracts the Euclidean skeleton of coronary vessels, and calculates the radius of coronary artery. Reconstruction result shows that the skeleton extracted by this arithmetic is complete, and the radius is estimated to be accurate.