Structural analysis and coding of multimodal medical images

摘要

Abstract: tive image coding scheme based on Discrete Cosine Transform (DCT) is considered. A set of 90 features in the spatial and spectral domain leads to a subset of features which is used to automatically classify subimages, taken from a multimodal medical image data base. The classifier, based on a binary decision tree, discriminates 13 classes. In the DCT domain, a normalization matrix for each class is generated using the features computed on subimages. This matrix allows to select the significant DCT coefficients associated to a class. This method leads to a performant adaptativity for the coding scheme. The classifier is very simple and cheap in computing time. A given subimage is classified, transformed with DCT, normalized by the matrix associated to its class, quantized and coded with Huffman tables.!6