一种多标记融合水平集的脑部多发性硬化斑块分割方法  

A multi-label fusion based level set method for multiple sclerosis lesion segmentation

作　　者：宫照煊 郭薇 张国旭[2] 郭佳[2] 朱振宇 覃文军 张国栋 GONG Zhaoxuan;GUO Wei;ZHANG Guoxu;GUO Jia;ZHU Zhenyu;TAN Wenjun;ZHANGGuodong(School of Computer Science,Shenyang Aerospace University,Shenyang 110136,P.R.China;The General Hospital of Shenyang Military,Shenyang 110016,P.R.China;Key Laboratory of Intelligent Computing in Medical Image,Ministry of Eucation,Shenyang 110819,P.R.China)

机构地区：[1]沈阳航空航天大学计算机学院,沈阳110136 [2]中国人民解放军沈阳军区总医院,沈阳110016 [3]医学影像智能计算教育部重点实验室,沈阳110819

出　　处：《生物医学工程学杂志》2019年第3期453-459,共7页Journal of Biomedical Engineering

基　　金：国家自然科学基金面上项目(61373088);国家自然科学基金青年项目(61402298);辽宁省自然科学基金项目(20170540702);沈阳航空航天大学校博士启动基金(18YB01)

摘　　要：通过对脑部磁共振影像(MRI)中硬化斑块形态、位置等信息的研究,本文提出了一种基于多标记融合水平集的脑部多发性硬化斑块分割方法。该方法首先使用模糊C均值模型提取初始硬化斑块区域,根据该区域的信息统计建立了灰度先验信息项和多标记融合项,并嵌入水平集模型中,通过水平集曲线演化实现脑部多发性硬化斑块分割。实验结果表明该方法能够有效分割脑部磁共振影像中的硬化斑块,具有较好的鲁棒性及较高的准确性。本文提出的方法极大地减轻了医师手动勾画硬化斑块区域的工作量,具有较大的临床应用价值。A multi-label based level set model for multiple sclerosis lesion segmentation is proposed based on the shape,position and other information of lesions from magnetic resonance image.First,fuzzy c-means model is applied to extract the initial lesion region.Second,an intensity prior information term and a label fusion term are constructed using intensity information of the initial lesion region,the above two terms are integrated into a region-based level set model.The final lesion segmentation is achieved by evolving the level set contour.The experimental results show that the proposed method can accurately and robustly extract brain lesions from magnetic resonance images.The proposed method helps to reduce the work of radiologists significantly,which is useful in clinical application.

关 键 词：磁共振影像 多发性硬化斑块 水平集 灰度先验信息 多标记融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]