International Journal of Engineering Research in Electronics and Communication Engineering (IJERECE)

Monthly Journal for Electronics and Communication Engineering

Volume4,March 2017,

Topic : Efficient Medical Image Compression based on Region of Interest

Authors:S. Muni Rathnam || T. Ramashri

Abstract:The Radiology places very high demands on the networking and digital storage infrastructure of hospitals. An Efficient method for segmentation and compression of medical images is proposed. In this paper we present a clustering algorithm called Adaptive Fuzzy C means for image segmentation which could be applied on general images and specific images (medical and microscopic images).This algorithm employs the concepts of fuzziness and belongingness to provide a better and more adaptive clustering process as compared to other clustering algorithm. Based on the results obtained the proposed algorithm gives better visual quality as compared to other clustering methods. Compression methods capable of delivering higher reconstruction quality for important parts are attractive in this situation. Only a small portion of the image might be diagnostically useful, but the cost of a wrong interpretation is high. Hence, for medical image compression and transmission Region based coding is necessary. In telemedicine applications Lossless compression schemes with secure transmission play a key role in accurate diagnosis and research. In this paper, we propose lossless scalable RBC for Medical images based on improved Ridgelet Transform and with distortion limiting compression technique for other regions in image. The main objective of this work is to reconstruct the image portions losslessly. For Medical images based on improved Ridgelet Transform and with distortion limiting compression technique for other regions in image. The main objective of this work is to reconstruct the image portions losslessly..

Keywords: Fuzzy C means; Image Compression; Improved Ridgelet transform; Morphological operations; ROI; MSE (means square error); PSNR (Peak signal to noise ratio).

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DOI: 01.1617/vol4iss3pid029


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