An automatic self‐initialized clustering method for brain tissue segmentation and pathology detection from magnetic resonance human head scans with graphics processing unit machine

Abstract

AbstractThe proposed work introduces a fully automatic modified fuzzy c‐means (MFCM) algorithm for segmenting brain tissue into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) which identifies the pathological conditions of magnetic resonance human head scans. The present work implements histogram smoothing using Gaussian distribution for finding the number of clusters (K) and cluster centers (C) to initialize modified FCM algorithm (MFCM). The modification includes the local impact of each pixel based on the median of local neighborhoods. This needs more computational power to reduce the processing time and requires a parallel programming environment like the Graphics Processing Unit. The parallel MFCM is performed with the help of compute unified device architecture language and reduced the processing time up to 80 speedup folds than the serial implementation in Matlab and 20 speedup folds than C programming implementation. The method is examined with the Internet Brain Segmentation Repository (IBSR20) T1W dataset. The quantitative and qualitative results of the proposed method are compared with state‐of‐the‐art‐methods using the Dice coefficient (DC). Proposed method yields high DC 0.84 ± 0.03 for GM, 0.83 ± 0.04 for WM, and 0.41 ± 0.12 for CSF segmentation. In post‐processing, 3D volumes of segmented regions have been constructed and compared with the gold standard quantitatively and qualitatively.