期刊名称：International Journal on Smart Sensing and Intelligent Systems
印刷版ISSN：1178-5608
出版年度：2015
卷号：8
期号：2
页码：1031-1049
出版社：Massey University
摘要：The most difficult and challenging problem in medical image analysis is image segmentation.Due to the limited imaging capability of magnetic resonance (MR), the sampled magnetic resonanceimages from clinic always suffer from noise, bias filed (also known as intensity non-uniformity), partialvolume effects and motive artifacts. In additional, for the complex shape boundary and topology ofbrain tissues and structures, segmenting magnetic resonance image of brain tumor fast, accurately androbustly is very difficult. In this paper, we propose an image segmentation algorithm based on multiconstrainsand dynamic prior. Through introducing a novel big scale constrain into Markov randomfiled model from magnetic resonance image we realize automatic segmentation under the principle ofmaximum a Posterior and a modified expectation-maximization algorithm according to the Bayesianframe. Finally, a set of human body detection and tracking experiments are designed to demonstratethe effectiveness of the proposed algorithms.
关键词：Brain tumor image; magnetic resonance; segmentation
