Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation

• Published in: Technology and health care Vol. 23; no. 1; pp. 23 - 35
• Main Authors: Blessy, S.A. Praylin Selva, Sulochana, C. Helen
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2015
• Subjects: Algorithms; Brain; Brain Neoplasms - diagnosis; Cluster Analysis; Clustering; Fuzzy; Fuzzy Logic; Humans; Image Interpretation, Computer-Assisted - methods; India; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Pattern Recognition, Automated; Segmentation; Sensitivity and Specificity; Task Performance and Analysis; Tumors; India; UOFC algorithm; segmentation; Fuzzy C-means; feature extraction; MRI brain tumor
• ISSN: 0928-7329; 1878-7401; 1878-7401
• DOI: 10.3233/THC-140876

Background: Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. Objective: To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Methods: Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Results: Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Conclusions: Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.