ROI Based Medical Image Compression for Telemedicine Application

As the medical imaging and telemedicine has been developing on large scale. With the increasing demand of storing and sending the medical images results in lack of sufficient memory spaces and transmission bandwidth. To fix these issues compression was introduced. Over the past few years in medical imaging lossless compression schemes are under intensive interest because there is no loss of information. The only small part is more useful out of the whole image. Region of Interest Based Coding techniques are more considerable in medical field for the sake of efficient compression and transmission. The current work begins with the pre-processing of medical image. Then segmentation is applied to divide the image into two parts i.e. ROI and non ROI. Finally compression is performed to reduce the storage and network bandwidth. In this paper Fractal lossy compression for Non ROI image and Context tree weighting lossless for ROI part of an image have been proposed for the efficient compression and compared with other such as Integer wavelet transform and Scalable RBC. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)