Citation

Ahamed Ayoobkhan, Mohamed Uvaze and Chikkannan, Eswaran and Ramakrishnan, Kannan (2018) Feed-Forward Neural Network-Based Predictive Image Coding for Medical Image Compression. Arabian Journal for Science and Engineering, 43 (8). pp. 4239-4247. ISSN 2191-4281, 2193-567X

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Abstract

The generation of high volume of medical images in recent years has increased the demand for more efficient compression methods to cope up with the storage and transmission problems. In the case of medical images, it is important to ensure that the compression process does not affect the image quality adversely. In this paper, a predictive image coding method is proposed which preserves the quality of the medical image in the diagnostically important region (DIR) even after compression. In this method, the image is initially segmented into two portions, namely, DIR and non-DIR portions using a graph based segmentation procedure. The prediction process is implemented using two identical feed- forward neural networks (FF-NNs) at the compression and decompression stages. Gravitational search and particle swarm algorithms are used for training the FF-NNs. Prediction is performed both in a lossless (LLP) and near lossless (NLLP) manner for evaluating the performances of the two FF-NN training algorithms. The prediction error sequence which is the difference between the actual and predicted pixel values is further compressed using a Markov model based arithmetic coding. The proposed method is tested using CLEF med 2009 database. The experimental results demonstrate that the proposed method is equipped for compressing the medical images with minimum degradation in the image quality. It’s found that the gravitational search method achieves higher PSNR values compared to the particle swarm and backpropagation methods.

Item Type:	Article
Uncontrolled Keywords:	Diagnostic imaging, Medical image compression, Feed-forwardneural network, Image coding, Graph-based segmentation
Subjects:	R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	09 Nov 2020 19:49
Last Modified:	09 Nov 2020 19:49
URII:	http://shdl.mmu.edu.my/id/eprint/7290

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