基于Hessian矩阵的冠脉造影图像分割与骨架提取

针对冠脉造影图像模糊、对比度低等导致的冠脉血管提取不完整、骨架不连续等问题，提出一种基于Hessian矩阵的多尺度冠脉分割与骨架提取算法，并估计血管半径值，为冠脉结构的三维重建奠定基础。该方法利用Hessian矩阵特征值对应线性目标的关系，构造一个新颖的血管相似性响应函数，对冠脉增强并阈值化得到分割结果，同时由 Hessian 矩阵确定冠脉血管的法线方向,通过求解法线方向上的极值点得到冠脉骨架的初始像素点集，以此来提取冠脉血管的欧氏骨架。实验结果表明,该算法简洁高效，相比现有算法能提取到更多的细小分支，得到的冠脉骨架完整，半径估计准确。

Coronary Angiography Image Segmentation and Skeleton Extraction Based on Hessian Matrix

Due to incompletely extracted coronary vessels and discontinuous skeleton caused by image blurring and low contrast of coronary angiogram, a multi-scale coronary segmentation and skeleton extraction method is proposed based on the Hessian matrix. Vessel radius to be estimated also lays the foundation of three-dimensional reconstruction of coronary structure. By using the relations between Hessian matrix eigenvalues and line items, a novel vesselness measure is constructed and obtain a segmented result enhancing and thresholding coronary are segmented. The normal direction of the coronary vessels is determined by the Hessian matrix, and an initial set of coronary skeleton pixels is obtained by extreme points through solving the normal direction. Therefore, Euclidean skeleton of coronary vessels are extracted. The experiment result indicates that the algorithm is concise and effective. Also it can extract more tiny branches comparing with the existing algorithms. It can get a completed coronary skeleton and estimate radius accurately.