中国医疗设备2017，Vol.32Issue(10)：21-26,6.DOI:10.3969/j.issn.1674-1633.2017.10.006
基于自组织特征映射和梯度熵聚类的MR脑部图像分割新算法
Improving MR Brain Image Segmentation Using Self-Organizing Maps and Entropy-Gradient Clustering
摘要
Abstract
Objective This study aimed to present a novel unsupervised method for MR brain image segmentation based on self-organizing maps (SOMs) and genetic algorithms (GAs). Methods In particular, the proposed method was based on five stages consisting of image preprocess, extracting first and second order statistical features, feature selection using evolutionary computation, voxel classification using SOM, and entropy-gradient (EG) clustering. Results Both simulated and clinical datasets were evaluated by different methods. Qualitative analysis showed that the components of the white matter, gray matter and cerebrospinal fluid were well preserved, and globally all the regions were correctly classified. Quantitative evaluation results showed that the genetic algorithms can achieve optimized feature set than principal component analysis (PCA). EG had a statistical significance difference with K-means (P<0.01). Our algorithm outperformed the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Conclusion The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes, which can provide better robustness, superiority, and pervasiveness in clinical applications.关键词
脑疾病/MR脑部图像/图像分割/自组织特征映射/遗传算法/梯度熵聚类Key words
cerebral disease/MR brain image/image segmentation/self-organizing maps/genetic algorithms/entropy-gradient clustering分类
信息技术与安全科学引用本文复制引用
丁力,周啸虎,陈宇辰,高伟..基于自组织特征映射和梯度熵聚类的MR脑部图像分割新算法[J].中国医疗设备,2017,32(10):21-26,6.基金项目
国家自然科学青年基金(81601477). （81601477）
