Quality Assessment of Compressed MR Medical Images using General Regression Neural Network
Bheshaj Kumar, Ganesh Ram Sinha, Kavita Thakur · 2011
Abstract: Medical imaging plays a major role in contemporary health care, both as a tool in primary diagnosis and as a guide for surgical and therapeutic procedures. Compression of radiological images is an effective mechanism for storage and transmission. The use of such images for teleradiology is of increasing importance, with one of the main reasons being the ability to call upon remotely located diagnostic experts. Whilst many researchers have addressed the problem of how the degradation of image quality with compression ratio affects observer-based diagnostic accuracy. In this work, we propose a quantitative analysis of quality for lossy compressed magnetic resonance (MR) images, and their influence in automatic tissue classification. Peak Signal to Noise Ratio (PSNR), as quality measurement, is not enough if we have medical images. So we need to find out new quality measurements not based in perception to make a quantitative analysis for medical image compression. This paper proposed a scheme which combines the Artificial Neural Network and technique of Structure Similarity Index Measurement (SSIM) to improve the issues. We feel that the proposed framework is in the right direction towards the achievement of this goal.