基于模糊连接度的帕金森病靶区核团识别技术  

Target Nuclei Segmentation in Parkinson's Disease Based on Fuzzy Connectedness

作　　者：杨春兰[1] 薛艳青[1] 王倩[1] 高宏建[1] 吴水才[1] 卢旺盛[2] 

机构地区：[1]北京工业大学生命科学与生物工程学院,北京100124 [2]中国人民解放军海军总医院神经外科,北京100048

出　　处：《北京工业大学学报》2014年第1期131-138,共8页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(81101107);北京市自然科学基金资助项目(3112005)

摘　　要：由于帕金森病靶区多为体积较小且结构边界不明显的核团,在MRI影像中不易准确分辨,对帕金森病靶区核团的分割进行初探,通过构建依赖树模型,对帕金森病相关子结构进行分割,提出一种有望用于帕金森病靶区核团的分割方法.首先针对图像模糊性的特点利用模糊连接度方法实现帕金森病关键核团的分割,然后运用数学形态学方法对分割结果进一步优化.通过与医院专家手工分割结果比较,该算法分割相似率在80%以上,能满足临床要求.The precise localization of the target structures is the key issue of the treatment of Parkinson＇s disease. However, these structures were mostly small, blur, and undistinguishable in MR images. In this paper, the segmentation of the target nucleus in Parkinson＇s disease was studied preliminary. Through constructing the dependence tree model, we segmented the substructures associated with Parkinson＇s disease. Theory of fuzzy conneetedness was used in the segmentation, and then morphology algorithm was used to optimize the results, so that the segmentation result was more desirable. Through compared with the manual segmentation results from hospital experts, the similarity index of algorithm segmentation is over 80% , which can satisfy the clinical requirements.

关 键 词：帕金森病 靶区 模糊连接度 

分 类 号：R318[医药卫生—生物医学工程]