Lossless Compression Algorithm for Medical Image Sequences with Selective Inter-Frame Redundancy Reduction

Abstract

the increasing number and resolution of computed tomography examinations place a growing burden on medical image archives, while lossless compression is required for diagnostic images. The lossless compression schemes specified by the medical image interchange standard are intra-frame and do not exploit correlations between adjacent slices. Existing inter-frame methods, in turn, typically select a prediction mode for an entire frame and therefore do not account for the distribution of homogeneous and edge regions within the frame. We developed a lossless coding algorithm for medical image series that extends JPEG-LS with selective inter-frame decorrelation of prediction errors. We partitioned each frame into non-overlapping 8 × 8-pixel blocks and identified blocks containing anatomical boundaries using the Canny edge detector. For each selected block, we searched the preceding frame for a spatially corresponding block using normalized cross-correlation. We then encoded the difference between the prediction errors of the current and reference blocks, thereby reducing the variance of the values supplied to the entropy coder. Experiments on 18 computed tomography series comprising 6,213 frames showed mean compression-ratio gains of 18.44% over JPEG-LS and 22.81% over JPEG2000.