Comparison of Hybrid Codes for MRI Brain Image Compression

Abstract

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scanning techniques produce human body pictures in digital form, which are prohibitive in nature. Hence compression is necessary for storage and transmission purposes of such medical images. In general, medical images are compressed in a lossless manner in order to preserve details and to avoid wrong diagnosis. But this leads to a lower compression rate. Hence we consider region of interest (ROI) normally the abnormal region in the image and compress it without loss to achieve high compression ratio in par with maintaining high image quality and the Non-region of interest (Non-ROI) of the image is compressed in a lossy manner. This paper discusses two simple hybrid coding techniques (Hybrid A and Hybrid B) on MRI human brain tumor image datasets. Also we evaluate their performance by comparing them with the standard lossless technique JPEG 2000 in terms of compression ratio (CR) and peak to signal noise ratio (PSNR). Both hybrid codes have resulted in computationally economical scheme producing higher compression ratio than existing JPEG2000 and also meets the legal requirement of medical image archiving. The results obtained prove that our proposed hybrid schemes outperform existing schemes.