Medical Image Compression using Neural Network with HGAPSO Optimization

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 %.

Significant medical image compression techniques: a review

“…Studies [10], [35] and [36] used neural network algorithms, which are characterised by speed of delivery and accuracy of results. At the same time, there are many disadvantages associated with their use, such as the network's need for training to reach the best results, as well as the fact that the number of mock models for each type of medical image must be increased to obtain good accuracy in compression and the adoption of good initial values to exceed the additional time in the calculation and length of implementation.…”

“…Eventually, the two regions are merged at low bit rates in order to achieve a reasonable visual quality of the processed medical image. Ravikiran and Jayanth [10] focused on developing a feed-forward neural network for medical image compression using the hybrid genetic and particle swarm optimization (HGAPSO) technique to optimise weights. HGAPSO is used in this technique to overcome the back-propagation algorithm (BPA) disadvantage.…”

“…Lossless compression methodologies are used in most applications that have precise requirements, such as medical imaging. These techniques include run-length coding, Huffman coding, Lempel-Ziv-Welch (LZW) coding and area coding [10]. In most applications, lossy compression methods give greater compression ratios than lossless techniques and are used when image quality is not a problem.…”