Brunenberg Ellen, Pujol Oriol, ter Haar Romeny Bart, Radeva Petia
Department of Biomedical Engineering, Eindhoven University of Technology, P.O.Box 513, 5600 MB Eindhoven, The Netherlands.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):9-16. doi: 10.1007/11866763_2.
Since the upturn of intravascular ultrasound (IVUS) as an imaging technique for the coronary artery system, much research has been done to simplify the complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was done. The first step was the extraction of texture features. The resulting feature space was used for classification, constructing a likelihood map to represent different coronary plaques. The information in this map was organized using a recently developed geodesic snake formulation, the so-called Stop & Go snake. The novelty of our study lies in this last step, as it was the first time to apply the Stop & Go snake to segment IVUS images.
自从血管内超声（IVUS）作为一种用于冠状动脉系统的成像技术兴起以来，人们已经开展了大量研究，以简化对所得图像的复杂分析。在本研究中，尝试开发一种用于IVUS图像的自动组织表征算法。第一步是提取纹理特征。所得特征空间用于分类，构建似然图以表示不同的冠状动脉斑块。使用最近开发的测地线蛇形公式（即所谓的“停走蛇形”）来组织此图中的信息。我们研究的新颖之处在于最后这一步，因为这是首次将“停走蛇形”应用于分割IVUS图像。
