Model-based image compression framework for CT and MRI images

This paper presents a novel model-based compression method for medical images. Unlike existing model-based compression methods, the proposed method is truly lossless and provides good compression ratio. The proposed method entails two major operations: registration of model and input (to be compressed) images, and compression of residual image (difference of model and input image). A context-based registration technique is proposed that depends on the criterion of minimising the residual image size. A quadtree-based adaptive block partitioning with rearrangement (QABPR) compression scheme is used to compress the residual image. Comparison with existing medical image compression methods and standard lossless compression techniques shows promising results.

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