Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation |
| |
Authors: | Mohamad Forouzanfar Nosratallah Forghani Mohammad Teshnehlab |
| |
Affiliation: | 1. Artificial Intelligent Lab, Department of Electrical Engineering of K. N. Toosi University of Technology, Tehran, Iran;2. Department of Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran;3. Department of Electrical Engineering of K. N. Toosi University of Technology, Tehran, Iran;4. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Australia |
| |
Abstract: | A traditional approach to segmentation of magnetic resonance (MR) images is the fuzzy c-means (FCM) clustering algorithm. The efficacy of FCM algorithm considerably reduces in the case of noisy data. In order to improve the performance of FCM algorithm, researchers have introduced a neighborhood attraction, which is dependent on the relative location and features of neighboring pixels. However, determination of degree of attraction is a challenging task which can considerably affect the segmentation results.This paper presents a study investigating the potential of genetic algorithms (GAs) and particle swarm optimization (PSO) to determine the optimum value of degree of attraction. The GAs are best at reaching a near optimal solution but have trouble finding an exact solution, while PSO’s-group interactions enhances the search for an optimal solution. Therefore, significant improvements are expected using a hybrid method combining the strengths of PSO with GAs, simultaneously. In this context, a hybrid GAs/PSO (breeding swarms) method is employed for determination of optimum degree of attraction. The quantitative and qualitative comparisons performed on simulated and real brain MR images with different noise levels demonstrate unprecedented improvements in segmentation results compared to other FCM-based methods. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|