Firefly algorithm-assisted segmentation of brain regions using tsallis entropy and markov random field

Abstract

In recent years, segmentation of medical images attracted the research community because of its significance in medical discipline. In this paper, firefly algorithm and Tsallis entropy-based approach is initially considered to threshold the standard brain MRI dataset. Later, the brain regions, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CF), are segmented using the Markov random field (MRF) approach. The proposed work is implemented using 256 × 256 sized benchmark MRI data, of subjects CHIMIC, JANPRZ, and JATKAM. Performance of the proposed approach is validated using a numerical metric that estimates the silhouette index of the estimated clusters. The proposed approach is also tested on other brain MRI dataset available in the literature and obtained better result in the segmentation of WM, GM, and CF. The simulation results in this study confirms that the proposed method offers an average enhancement of cluster classification by 4.44% in terms of silhouette index.