Improvement of CSF, WM and GM Tissue Segmentation by Hybrid Fuzzy — Possibilistic Clustering Model based on Genetic Optimization Case Study on Brain Tissues of Patients with Alzheimer’s Disease

• Published in: The International journal of networked and distributed computing (Online) Vol. 6; no. 2; pp. 63 - 77
• Main Authors: Lazli, Lilia, Boukadoum, Mounir
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2018; Springer
• Subjects: Alzheimer’s disease; Brain tissue clustering; Fuzzy c-means algorithm; Genetic algorithms; Hybrid reasoning; Possibilistic c-means algorithm; Research Article; Fuzzy c-means algorithm; Alzheimer’s disease; Hybrid reasoning; Possibilistic c-means algorithm; Brain tissue clustering; Genetic algorithms
• ISSN: 2211-7938; 2211-7946; 2211-7946
• DOI: 10.2991/ijndc.2018.6.2.2

Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. Fuzzy C-mean (FCM) is one of the most popular clustering based segmentation methods. However FCM does not robust against noise and artifacts such as partial volume effect (PVE) and inhomogeneity. In this paper, a new approach for robust brain tissue segmentation is described. The proposed method quantifies the volumes of white matter (WM), gray matter (GM) and cerebrospinal fluid(CSF) tissues using hybrid clustering process which based on: (1) FCM algorithm to get the initial center partition. (2) Genetic algorithms (GA) to achieve optimization and to determine the appropriate cluster centers and the fuzzy corresponding partition matrix. (3) Possibilistic C-Means (PCM) algorithm for volumetric measurements of WM, GM, and CSF brain tissues. (4) Rule of the possibility maximum to compute the labeled image in decision step. The experiments were realized using different real and synthetic brain images from patients with Alzheimer’s disease. We used Tanimoto coefficient, sensitivity and specificity validity indexes to validate the proposed hybrid approach and we compared the performance with several competing methods namely FCM and PCM algorithms. Good result was achieved which demonstrates the efficiency of proposed clustering approach and that it can outperforms competing methods especially in the presence of PVE and when the noise and spatial intensity inhomogeneity are high.