Region-of-Interest Based Compression of Magnetic Resonance Imaging Data

Publisher Summary The aim of this chapter is to propose an efficient coding scheme that takes advantage of the difference in visual importance among areas of the same image, classifies them in distinct categories, and reproduces the image with variable spatial reconstruction quality. Depending on the percentage of regions of interest (ROI) to low importance regions, substantial saving can be achieved both for storage and transmission use. The chapter discusses that progressive discrete cosine transform (DCT) coding can be smoothly interwoven with the use of regions of interest to increase the compression ratios obtained in a variety of cases, while causing user-acceptable degradation in image quality. A ROI-JPEG coder provides the means for encoding regions of low/high interest in the image by differentiating the quantization tables among these areas. This goal is achieved by using different quantization quality factors (QF), as defined in the baseline JPEG algorithm, for each category of image regions. Additionally, for off-line storage applications, visually optimal quantization tables on a bits/pixel target rate basis can be computed for both the high interest and background regions of the image. The classification of image regions into ROI and background regions can be implemented either through unsupervised automated procedures or by user interactivity.