Contextual Compression of Ultrasound Medical Images

Medical image compression addresses the issues of larger storage and faster transmission requirements. Contextual compression of Ultrasound (US) medical images aims at compressing the diagnostically important region, region of interest (RoI), of an image with supreme quality as compared to rather unimportant area i.e. the background. Thus, the RoI area is compressed with less compression ratio and the background with the highest possible compression ratio in order to get better overall compression performance. As a part of contextual compression technique four compression algorithms viz. JPEG coding, Wavelet Transform coding, JPEG 2000 coding and the SPIHT coding algorithms have been implemented in the present work. These algorithms have been implemented using MATLAB, Image Processing and Wavelet toolbox, in particular. A detailed analysis on the basis of parameters like mean square error (MSE), peak signal to noise ratio (PSNR) and correlation coefficient (CoC) has been carried out to evaluate these algorithms.

Prof. Rahul K. Kher is with the EC department of G H Patel college of Engg. & Tech, Vallabh Vidyanagar, India as an Associate Professor. He received his B.E. (Electronics) degree from Sardar Patel University in 1997 and M. Tech degree from IIT, Roorkee in 2006. Currently he is pursuing Ph. D. in ECG Signal Processing from Sardar Patel University.