Volume 5, Issue 10 (October 2018), Pages: 106-114
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Original Research Paper
Title: Biomedical images enhancement based on swarm optimization and differential evolution technique
Author(s): Abdullah Shawan Alotaibi *
Affiliation(s):
Computer Science Department, Shaqra University, Shaqra, Saudi Arabia
https://doi.org/10.21833/ijaas.2018.10.015
Full Text - PDF XML
Abstract:
In this paper, we have introduced an effective technique to remove the noise in the MRI and CT medical images during the process of acquisition, transmission, storage or compression. Removing these noise from medical images must be done without affecting relevant features of the image. Many techniques, such as genetic algorithm, Particle Swarm Optimization, Dynamic Multi-Swarm Particle Swarm Optimization and matching pursuit algorithm are used for denoising MRI and CT images. These techniques need more time to remove noise from medical images and find local point optimal. The proposed Differential Evolution based on Matching Pursuit (DE-MP) is used to detect best atom dictionary. The initial dictionary is created from an anisotropic atom. To evaluate the performance of the proposed techniques, the results of the proposed algorithm were compared with the other algorithm. The numerical results show that the performance of the proposed algorithm is more efficient and faster than other algorithms for medical images denoising.
© 2018 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Matching pursuit, Differential evolution, Anisotropic atom, Genetic algorithm, Particle swarm optimization, Dynamic multi-swarm optimization
Article History: Received 10 May 2018, Received in revised form 14 August 2018, Accepted 26 August 2018
Digital Object Identifier:
https://doi.org/10.21833/ijaas.2018.10.015
Citation:
Alotaibi AS (2018). Biomedical images enhancement based on swarm optimization and differential evolution technique. International Journal of Advanced and Applied Sciences, 5(10): 106-114
Permanent Link:
http://www.science-gate.com/IJAAS/2018/V5I10/Alotaibi.html
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