Fuzzy entropy‐based MR brain image segmentation using modified particle swarm optimization |
| |
| Authors: | R Krishna Priya C Thangaraj C Kesavadas S Kannan |
| |
| Affiliation: | 1. Department of Computer Science, Kalasalingam University, , Virudhunagar, Tamil Nadu, India;2. Former Vice Chancellor, Anna University of Technology, , Chennai, Tamil Nadu, India;3. Department of Imaging Sciences and Interventional Radiology, Sree Chitra Thirunal Institute for Medical Sciences and Technology, , Trivandrum, Kerala, India;4. Department of Electrical and Electronics Engineering, Kalasalingam University, , Virudhunagar, Tamil Nadu, India |
| |
| Abstract: | This article presents an image segmentation technique based on fuzzy entropy, which is applied to magnetic resonance (MR) brain images in order to detect brain tumors. The proposed method performs image segmentation based on adaptive thresholding of the input MR images. The image is classified into two membership functions (MFs) of the fuzzy region: 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 fuzzy entropy. Through a number of examples, The performance is compared with existing entropy based object segmentation approaches and the superiority of the proposed method is demonstrated. The experimental results are compared with the exhaustive search method and Otsu's segmentation technique. The result shows the proposed fuzzy entropy‐based segmentation method optimized using MPSO achieves maximum entropy with proper segmentation of infected areas and with minimum computational time. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 281–288, 2013 |
| |
| Keywords: | fuzzy entropy Magnetic Resonance Image Fuzzy Membership function |
|
|
