An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques |
| |
| Affiliation: | 1. Department of Electronics & Telecommunication Engineering VSS University of Technology, Burla, Odisha, 768018, India;2. Depatment of Electronics & Communication Engineering, ABIT, Cuttack, Odisha, India;3. Machine Intelligence Research Labs (MIR Labs) Scientific Network for Innovation and Research Excellence, WA 98071-2259, USA;2. Faculty of Organizational Sciences (FOS), University of Belgrade, Belgrade, Serbia;3. Shools of Education and Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom;1. Dep. de Computação e Construção Civil – Centro Federal de Educação Tecnológica de Minas Gerais, R. Dezenove de Novembro, 121, Centro, Timóteo, MG, Brazil;2. Programa de Pós-Graduação em Engenharia Elétrica – Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil;3. Dep. de Física e Matemática – Centro Federal de Educação Tecnológica de Minas Gerais, Av. Amazonas, 7675, Nova Gameleira, Belo Horizonte, MG, Brazil;4. Dep. de Matemática – Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, 6627, Pampulha, Belo Horizonte, MG, Brazil;1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, PR China;2. College of Mathematic Science, Liaocheng University, Liaocheng 252059, PR China;1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China;2. School of Electrical Engineering and Information Engineering, LanZhou University of Technology, Lanzhou 730050, China |
| |
| Abstract: | The conventional two dimensional (2-D) histogram based Otsu’s method gives unreliable results while considering multilevel thresholding of brain magnetic resonance (MR) images, because the edges of the brain regions are not preserved due to the local averaging process involved. Moreover, some of the useful pixels present inside the off-diagonal regions are ignored in the calculation. This article presents an evolutionary gray gradient algorithm (EGGA) for optimal multilevel thresholding of brain MR images. In this paper, more edge information is preserved by computing 2-D histogram based gray gradient. The key to our success is the use of the gray gradient information between the pixel values and the pixel average values to minimize the information loss. In addition, the speed improvement is achieved. Theoretical formulations are derived for computing the maximum between class variance from the 2-D histogram of the brain image. A first-hand fitness function is suggested for the EGGA. A novel adaptive swallow swarm optimization (ASSO) algorithm is introduced to optimize the fitness function. The performance of ASSO is validated using twenty three standard Benchmark test functions. The performance of ASSO is better than swallow swarm optimization (SSO). The optimum threshold value is obtained by maximizing the between class variance using ASSO. Our method is tested using the standard axial T2 ? weighted brain MRI database of Harvard medical education using 100 slices. Performance of our method is compared to the Otsu’s method based on the one dimensional (1-D) and the 2-D histogram. The results are also compared among four different soft computing techniques. It is observed that results obtained using our method is better than the other methods, both qualitatively and quantitatively. Benefits of our method are – (i) the EGGA exhibits better objective function values; (ii) the EGGA provides us significantly improved results; and (iii) more computational speed is achieved. |
| |
| Keywords: | Multilevel thresholding MRI brain Soft computing techniques Otsu’s method |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
