基于MRF的膝关节半月板快速自动分割算法  

Fast and automatic segmentation algorithm of knee menisci based on Markov random field

作　　者：詹曙[1] 林芬华[1] 郝世杰[1] 蒋建国[1] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009

出　　处：《电子测量与仪器学报》2009年第12期55-60,共6页Journal of Electronic Measurement and Instrumentation

基　　金：教育部博士点基金(编号:20060359004)资助项目;教育部留学归国人员科研启动基金(编号:413117)资助项目

摘　　要：核磁共振成像(magnetic resonance imaging,MRI)图像形态、纹理均较为复杂,从图像中分割出感兴趣组织结构具有一定难度。提出一种"分割-粗定位-提取"思路,充分利用MRI成像特征和膝关节解剖学的先验知识,快速、全自动地精确分割形态复杂、尺寸细小的膝关节半月板:首先利用多尺度马尔可夫随机场(Markov random field,MRF)方法自动、快速地分割与目标有相似灰度分布的组织结构,然后结合sobel算子和直方图投影方法粗定位半月板区域,最后通过判断连通区域面积提取出精确的半月板区域。实验结果表明,与目前手动、半自动的半月板分割等研究工作相比,可以客观可重复地分割出半月板前后角等区域,并且算法耗时极低。Magnetic Resonance Imaging （MRI） has complex contents in both morphology and texture, from which impose difficulty on effective image segmentation. Therefore, the strategy of ＂segmenting-locating-extracting＂ is proposed, where MRI features and knee anatomical knowledge are made the most of as priori information. Firstly, multi-scale Markov Random Field method is used to implement an automatic and fast segmentation of tissues that have similar intensity distribution as menisci. Then the meniscus region is roughly located by combining sobel operator with histogram projection. Finally, the areas of connective regions to extract the segmented meniscus anterior and posterior horns are determined accurately. Compared with related work on manually or semi-automatically segmenting menisci, the experiments show that the proposed algorithm automatically can performs a repeatable and accurate segmentation on menisci, with extremely low temporal cost.

关 键 词：核磁共振成像 图像分割 多尺度MRF 半月板 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]