Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

作     者：Ji-jun TONG Peng ZHANG Yu-xiang WENG Dan-hua ZHU 

作者机构：School of lnformation Science and TechnologyZhejiang Sci-Tech UniversityHangzhou 310018China Department of NeurosurgeryThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China State Key Laboratory for Diagnosis and Treatment of lnfectious DiseasesCollaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2018年第19卷第4期

页      面：471-480页

核心收录：

学科分类：12[管理学] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 0835[工学-软件工程] 100214[医学-肿瘤学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：Project supported by the National Natural Science Foundation of China(No.31200746) the Zhejiang Provincial Key Research and Development Plan,China(No.2015C03023) the‘521’Talent Project of ZSTU,China 

主　　题：Brain tumor segmentation Kernel method Sparse coding Dictionary learning 

摘      要：The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging(MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. Sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of m×m×m around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value(PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region(dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core(dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor(dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.