Affiliations: [a] Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Karungal, Kanyakumari, Tamil Nadu, India | [b] Department of Electronics and Communication Engineering, St.Xavier's Catholic College of Engineering, Nagercoil, Kanyakumari, Tamil Nadu, India
Correspondence:
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Corresponding author: S.A. Praylin Selva Blessy, Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Karungal, Kanyakumari, Tamil Nadu, India. E-mail: praylinstalin@gmail.com.
Abstract: 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.
