Contextual Compression of Ultrasound Medical Images
Comparative Analysis of Various Image Compression Techniques
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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 ...
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.
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