计算机与数字工程2017，Vol.45Issue(7)：1391-1396,1401,7.DOI:10.3969/j.issn.1672-9722.2017.07.034
基于SVM和有监督描述子学习算法的脑MR图像颅骨分割方法
Automated Segmentation Based on Support Vector Machine and Supervised Descriptor Learning from Brain MR Image
摘要
Abstract
A solution of EEG/MEG forward problem is essential and important in stereotactic neurosurgery applications.It is necessary to build a multi-layer brain model to distinguish different tissues for MEG/EEG forward problem.Although soft tissues can be clearly seen in MR images,but the intensity of skull is so low because of a lack of hydrogen in skull that can't be segmented automatically and accurately from MR image.Extracting skull form MR image automatically end up to be a key problem when calculating the MEG/EEG forward problem.In order to solve the above problem,a support vector machine(SVM) is proposed based segmentation algorithm using global features and local features of MR image.Moreover,the supervised descriptor learning(SDL) algorithm is combined that can transform the feature matrix into a compact one,and finally the skull from brain MR image is extrated by training on multi-modal images from the same patient whose CTs and MRs are available.Compared to the algorithm based on SVM only and mathematical morphology based algorithm,the proposed method shows a considerable improvement on segmentation accuracy.The proposed method achieves an accuracy with Dice coefficient 0.832 compared with the other two methods 0.798 and 0.482.The proposed hybrid algorithm extract the skull successfully,so that the EEG,MEG source imaging problem can be solved easily in future work.关键词
颅骨分割/支持向量机/有监督描述子学习算法/特征提取/特征压缩Key words
skull segmentation/support vector machine (SVM)/supervised descriptor learning (SDL)/feature extraction/feature compression分类
信息技术与安全科学引用本文复制引用
黄勇其,史文博,周志勇,庞树茂,佟宝同,赵凌霄,戴亚康..基于SVM和有监督描述子学习算法的脑MR图像颅骨分割方法[J].计算机与数字工程,2017,45(7):1391-1396,1401,7.基金项目
中国科学院百人计划项目 （）
国家自然科学基金(编号:61301042) （编号:61301042）
国家863计划(编号:2015AA020514) （编号:2015AA020514）
国家自然科学基金青年基金项目(编号:61501452) （编号:61501452）
江苏省博士后基金项目(编号:1501089C)资助. （编号:1501089C）
