Medical Image Compression using Neural Network with HGAPSO Optimization
Ravikiran H.K1, Jayanth J2
1Ravikiran H.K., Department of Electronics and Communication, GSSS Institute of Engineering and Technology for Women, Affiliated to Visvesvaraya Technological University, Mysuru, India.
2Dr. Jayanth J, Department of Electronics and Communication, GSSS Institute of Engineering and Technology for Women, Affiliated to Visvesvaraya Technological University, Mysuru, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on 28 November, 2019. | Manuscript published on December 10, 2019. | PP: 3505-3509 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6616129219/2019©BEIESP | DOI: 10.35940/ijitee.B6616.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Lossy medical image compression has become increasingly attractive due to a drastic increase in the number of images used for diagnosis and treatment. The work focused on developing a feed-forward neural network for compression of medical images with optimization of weights using hybrid genetic and particle swarm (HGAPSO) optimization technique. The neural network can achieve a better compressed & decompressed image only with the proper training and optimized weights. Training algorithms such as back-propagation algorithm (BPA) traps to local minima rather than the global one which degrades the quality of a reconstructed image. In this work, HGAPSO optimization is adopted to overcome the drawback of BPA. HGAPSO parameters are carefully chosen to have better exploitation & exploration in the search area, which avoids the algorithm from trapping to the local minima. High-quality results of Genetic Algorithm (GA) obtained using selection, crossover and mutation can provide quality guidance for PSO which improves the results of the proposed system. The performance of the proposed work is evaluated on a raw medical image database based on PSNR, MSE, and CR. The experiment is simulated for 16-4-16 neural network architecture and a compression ratio of 75 % is achieved. The results obtained indicated that with proper training PSNR could be improved by 1.98 %.
Keywords: Artificial Neural Network, Genetic Algorithm and particle Swarm Optimization, Image Compression
Scope of the Article: Algorithm Engineering