Human chromosome image compression using cascaded differential and wavelet coding
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Chromosome analysis is an important procedure in clinical and cancer cytogenetics. Efficient image compression techniques are highly desired to accommodate the rapid growth in the use of digital media for archiving, storage and communication of chromosome spread images. In this paper, we propose a new method based on an important characteristic of these images: The regions of interest to cytogeneticists for evaluation and diagnosis are well determined and segmented. Such information is utilized to advantage in our compression algorithm, which combines lossless coding of chromosome regions with lossy-to-lossless coding of the remaining image parts. This is accomplished by first performing a differential operation on chromosome regions for decorrelation, followed by critically sampled integer wavelet transforms on these regions and the remaining image parts. A modified set partitioning in hierarchical trees (SPIHT)1 algorithm is then used to generate separate embedded bitstreams that allow continuous lossy-to-lossless compression of both chromosome regions and the rest of the image (although lossless coding of the former is commonly used in practice). Experiments on sample chromosome spread images indicate that the proposed approach significantly outperforms several reference compression schemes and the techniques currently employed in commercial systems.