Region of Interest Based MRI Brain Image Compression Using Peano Space Filling Curve

Title:Region of Interest Based MRI Brain Image Compression Using Peano Space Filling Curve

Volume: 11 Issue: 2

Author(s): C. Peter Devadoss, Balasubramanian Sankaragomathi and Thirugnanasambantham Monica

Affiliation:

Keywords: Region of interest, peano space filling curve, LZW, PSNR, CR.

Abstract: Applications of medical images are increasing rapidly, moreover the medical images produced by the imaging devices occupies more volume of space. Medical information system needs to store huge amount of data in the form of medical images for future record and also these images are required to be transmitted over network to the place of specialist to obtain diagnostics opinion. Because of the limited availability of space and bandwidth, an efficient compression technique is needed to reduce the bits to store and transmit these images. In this paper, we propose an efficient scheme of medical image compression based on region of interest using Peano space filling curve. In this method, region containing the most useful diagnostic features are treated as region of interest (ROI), pixels in ROI region are arranged using Peano space filling curve (PSFC) and entropy encoded without any loss in quality. The remaining regions are treated as non region of interest(NROI) and are encoded with singular value decomposition followed by entropy encoding. The encoded ROI and NROI region are combined to give the compressed output. The performance of the proposed compression technique with various encoding such as Huffman and LZW along with Peano Space Filling curve reordering is compared with standard JPEG2000 compression. The result shows that proposed method gives better performance in terms of PSNR and Compression Ratio.

Cite this article as:

Devadoss Peter C., Sankaragomathi Balasubramanian and Monica Thirugnanasambantham, Region of Interest Based MRI Brain Image Compression Using Peano Space Filling Curve, Current Signal Transduction Therapy 2016; 11 (2) . https://dx.doi.org/10.2174/1574362411666160616124516