Subband analysis and synthesis of volumetric medical images using wavelet

We present in this paper a study of subband analysis and synthesis of volumetric medical images using 3D separable wavelet transforms. With 3D wavelet decomposition, we are able to investigate the image features at different scale levels that correspond to certain characteristics of biomedical structures contained in the volumetric images. The volumetric medical images are decomposed using 3D wavelet transforms to form a multi-resolution pyramid of octree structure. We employ a 15-subband decomposition in this study, where band 1 represents the subsampled original volumetric images and other subbands represent various high frequency components of a given image. Using the available knowledge of the characteristics of various medical images, an adaptive quantization algorithm based on clustering with spatial constraints is developed. Such adaptive quantization enables us to represent the high frequency subbands at low bit rate without losing clinically useful information. The preliminary results of analysis and synthesis show that, by combining the wavelet decomposition with the adaptive quantization, the volumetric biomedical images can be coded at low bit rate while still preserving the desired details of biomedical structures.