Sparse Coding for Brain Tumor Segmentation Based on the Non-Linear Features

Abstract

The main aim of brain Magnetic Resonance Image (MRI) segmentation is to extractthe significant objects like tumors for better diagnosis and proper treatment. As the brain tumors are distinct in the sense of shapes, location, and intensity it is hard to define a general algorithm for the tumor segmentation. Accurate extraction of tumors from the brain MRIs is a challenging task due to the complex anatomical structure of brain tissues in addition to the existence of intensity inhomogeneity, partial volume effects, and noise. In this paper, a method of Sparse coding based on non-linear features is proposed for the tumor segmentation from MR images of the brain. Initially, first and second-order statistical eigenvectors of 3 × 3 size are extracted from the MRIs then the process of Sparse coding is applied to them. The kernel dictionary learning algorithm is employed to obtain the non-linear features from these processed vectors to build two individual adaptive dictionaries for healthy and pathological tissues. This work proposes dictionary learning based kernel clustering algorithm to code the pixels, and then the target pixelsare classified by utilizing the method of linear discrimination. The proposed technique is applied to several tumor MRIs, taken from the BRATS database. This technique overcomes the problem of linear inseparability produced by the high level intensity similarity between the normal and abnormal tissues of the given brain image. All the performance parameters are high for the proposed technique. Comparison of the results with some other existing methods such as Fuzzy – C- Means (FCM), Hybrid k-Mean Graph Cut (HKMGC) and Neutrosophic Set – Expert Maximum Fuzzy Sure Entropy (NS-EMFSE) demonstrates that the proposed sparse coding method is effective in segmenting the brain tumor regions.