MRI brain tumor segmentation based on texture features and kernel sparse coding. (January 2019)

• Record Type: Journal Article
• Authors: Tong, Jijun; Zhao, Yingjie; Zhang, Peng; Chen, Lingyu; Jiang, Lurong
• Is Part Of: Biomedical signal processing and control. Volume 47(2019)
• Page Start: 387
• Page End: 392
• Publication Date: 2019-01
• Subjects: Brain tumor segmentation -- Texture feature -- Kernel method -- Sparse coding -- Dictionary learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.06.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A brain tumor segmentation method for FLAIR sequences MRI images. Method is based on texture features and kernel sparse coding. Avoid linear inseparability from the similarity between normal and non-normal tissues. Abstract: An automatic brain tumor segmentation method based on texture feature and kernel sparse coding from FLAIR (fluid attenuated inversion recovery) contrast-enhanced MRIs (magnetic resonance imaging) is presented in this paper. First, the MRIs are pre-processed to reduce noise, enhance contrast and correct the intensity non-uniformity. Then sparse coding is performed on the first order and second order statistical eigenvector extracted from original MRIs which is a patch of 3 × 3 around the voxel. The kernel dictionary learning is used to extract the non-linear features to construct two adaptive dictionaries for healthy and pathologically tissues respectively. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels, then the linear discrimination method is used to classify the target pixels. In the end, the flood-fill operation is used to improve the segmentation quality. The results demonstrate that the method based on kernel sparse coding has better capacity and higher segmentation accuracy with low computation cost.