Brain Tumor Segmentation in MRI Images Using Integrated Modified PSO-Fuzzy Approach

Krishna Priya Remamany1, Thangaraj Chelliah2, Kesavadas Chandrasekaran3, and Kannan Subramanian4

1Department of Electrical and Computer Engineering, Caledonian College of Engineering, Oman

2Anna University of Technology, India

3Department of Imaging Sciences and Interventional Radiology, SCTIMST, India

4Department of EEE, Kalasalingam University, India

Abstract: An image segmentation technique based on maximum fuzzy entropy is applied for Magnetic Resonance (MR) brain images to detect a brain tumor is presented in this paper. The proposed method performs image segmentation based on adaptive thresholding of the input MR brain images. The MR brain image is classified into two Membership Function (MF), whose MFs of the fuzzy region are Z-function and S-function. The optimal parameters of these fuzzy MFs are obtained using Modified Particle Swarm Optimization (MPSO) algorithm. The objective function for obtaining the optimal fuzzy MF parameters is considered to be the maximum the fuzzy entropy. In the course of a number of examples, the performance is compared with those using existing entropy-based object segmentation approaches and the superiority of the proposed MPSO method is demonstrated. The experimental results are compared with the exhaustive search method and Otsu segmentation technique. The result shows the proposed fuzzy entropy based segmentation method optimized using MPSO achieves maximum entropy with proper segmentation of tumor and with minimum computational time.

Keywords: Fuzzy entropy, particle swarm optimization, MRI, segmentation.

Received June 9, 2012; Accepted April 18, 2013