计算机应用研究2017，Vol.34Issue(8)：2546-2550,2556,6.DOI:10.3969/j.issn.1001-3695.2017.08.069
基于改进粗糙集概率模型的鲁棒医学图像分割算法
Improved probability model of rough set basedrobust medical image segmentation algorithm
摘要
Abstract
Parametric model based image segmentation algorithms show low segmentation accuracy to complex medical images.This paper proposed an improved probability model of rough set based robust medical image segmentation algorithm to solve that problem.Firstly,it introduced lower approximation and probabilistic boundary region of rough set to expectation maximization algorithm to represent each cluster.Then it modeled intensity distribution of image as a mixed rough set probability distribution with finite number.Lastly, it incorporated the spatial information of image into Markov random field to enhance the robustness of the image segmentation algorithm.Both synthetic brain MR image database and real MR image database based segmentation experimental results show that the proposed algorithm has better performance in segmentation accuracy and robustness than other parametric model based image segmentation algorithms and other brain MR image segmentation algorithms.关键词
粗糙集/参数化模型/医学图像分割/最大期望算法/马尔可夫随机场/鲁棒性Key words
rough set/parametric model/medical image segmentation/expectation maximization algorithm/Markov random field/robustness分类
信息技术与安全科学引用本文复制引用
吴方,何尾莲..基于改进粗糙集概率模型的鲁棒医学图像分割算法[J].计算机应用研究,2017,34(8):2546-2550,2556,6.基金项目
福建省自然科学基金资助项目(2016J01373) （2016J01373）
福建省卫生厅青年科研计划资助项目(2013-1-34) （2013-1-34）
