A Coding Technique with Progressive Reconstruction Based on VQ and Entropy Coding Applied to Medical Images

In this paper we propose a novel lossless coding scheme for medical images that allows the final user to switch between a lossy and a lossless mode. This is done by means of a progressive reconstruction philosophy (which can be interrupted at will) so we believe that our scheme gives a way to trade off between the accuracy needed for medical diagnosis and the information reduction needed for storage and transmission. We combine vector quantization, run-length bit plane and entropy coding. Specifically, the first step is a vector quantization procedure; the centroid codes are Huffman-coded making use of a set of probabilities that are calculated in the learning phase. The image is reconstructed at the coder in order to obtain the the error image; this second image is divided in bit planes, which are then run-length and Huffman coded. An second statistical analysis is performed during the the learning phase to obtain the parameters needed in this final stage. Our coder is currently trained for hand-radiographs and fetal echographies. We compare our results for this two types of images to classical results on bit plane coding and the JPEG standard. Our coder turns out to outperform both of them.