International Journal of Electrical and Computer Engineering (IJECE) Vol. 8, No. 5, October 2018, pp. 2818~2828 ISSN: 2088-8708, DOI: 10.11591/ijece.v8i5.pp2818-2828  2818 Journal homepage: http://iaescore.com/journals/index.php/IJECE MICCS: A Novel Framework for Medical Image Compression Using Compressive Sensing Lakshminarayana M. 1 , Mrinal Sarvagya 2 1 Department of ECE, Visvesvaraya Technological University, India 2 School of ECE, REVA University, India Article Info ABSTRACT Article history: Received Mar 10, 2018 Revised Aug 8, 2018 Accepted Aug 26, 2018 The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-of-Interest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio. Keyword: Compressive sensing Image compression Lossless and lossy compression Medical image processing Quality of image Region of interest Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Lakshminarayana M., Department of Electronics and communication Engineering, Visvesvaraya Technological University, Belgaum, Karnataka, India. Email: lakshminarayana.m.2015@ieee.org 1. INTRODUCTION Uttarakhand deluge in India happened on June 2013, has affected destruction comprising breaking of roads, demolition of bridges, restaurants, hospitals, internet connectivity and communication network. As a consequence, about 5700 populates were murdered, and more than 110,000 travelers and tourists stuck in the valleys [1]. This kind of tragedy has strained ever growing responsiveness to refining rescue pains. One of the methods that can efficiently apply throughout tragedy retrieval is recognized as telemedicine in the literature [2] and [3]. Telemedicine is an amalgamation of data computing technology and medical data science. It is utilized to deliver medical data storage and transmission, medical consultation, remote guided robot surgery and many different healthiness care facilities to patients. The convergence of telecommunication technological experts and doctors will provide a platform to encourage next generation medical services. The amount of medical image data that is archived in a hospital is huge, so there is a stringent need to reduce the storage space and transfer time required by these pictures while maintaining the quality of medical image for the diagnostic purpose at the same time. Thus therapeutic image compression productions a significant part for the efficient deployment of next generation healthcare system [4]. Compressed sampling has witnessed a significant attention newly huge courtesy demand for fast, efficient and inexpensive in image and signal processing algorithms, solicitations and medical devices. It is a signal processing method for efficient collection and reconstructing an original signal, by analyzing solutions