A NEW METHOD FOR COMPUTED-TOMOGRAPHY IMAGE COMPRESSION USING ADJACENT SLICE DATA

RATIONALE AND OBJECTIVES. The authors developed and subjectively evaluated an interslice compression algorithm that explores the redundancy among adjacent slices of an x-ray computed tomography (CT) scan. This algorithm has been compared to an intraslice compression algorithm based on the two-dimensional discrete cosine transform. METHODS. Nine x-ray CT head images from three patients were compressed with this interslice method at compression ratios of 5:1, 10:1, and 15:1. The same images were also compressed with the intraslice method at the same ratios. Six radiologists judged quality of randomly selected compressed and decompressed images compared to that of the originals. The evaluation data were analyzed statistically with the analysis of variance and Tukey's multiple comparison. Kappa-like statistics (Williams index and O'Connell and Dobson indexes) were also calculated to measure the agreement among readers beyond the amount expected by chance. RESULTS. The interslice coding algorithm showed significantly better quality than the intraslice method at significance level 0.05, even though there was no difference in the objective distortion measure (signal-to-noise ratio). Also, the quality of 10:1 compressed images with the interslice coding algorithm was not significantly different from that of the originals at level 0.05. While large variations in agreement occurred among readers, the overall agreement was statistically significant. CONCLUSIONS. By using adjacent slice information in compressing x-ray CT images, significantly better quality in compressed and decompressed images was achieved. While 10:1 compressed images with the interslice algorithm were not significantly different from the originals in quality at level 0.05, effect on diagnostic accuracy remains to be investigated.