An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

作     者：Lei Ling Lijun Huang Jie Wang Li Zhang Yue Wu Yizhang Jiang Kaijian Xia 

作者机构：School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi214122China Department of Scientific ResearchChangshu Hospital Affiliated to Soochow UniversityChangshu215500China China Department of Biomedical EngineeringFaculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第137卷第12期

页      面：2353-2379页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：This work was supported in part by the National Natural Science Foundation of China under Grant 62171203 in part by the Suzhou Key Supporting Subjects[Health Informatics(No.SZFCXK202147)] in part by the Changshu Science and Technology Program[No.CS202015,CS202246] in part by the Changshu City Health and Health Committee Science and Technology Program[No.csws201913] in part by the“333 High Level Personnel Training Project of Jiangsu Province”. 

主　　题：Soft subspace clustering image segmentation genetic algorithm generalized noise brain MR images 

摘      要：In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.