Medical image compression using principal component analysis

We describe a coding scheme based on principal component analysis to compress medical images. The region of interest (tissue region) is first located. The background area can then be coded as simple models. In this situation the compression ratio can be quite high. As for the region of interest, more sophisticated algorithm will be required to achieve a high compression ratio and preserve necessary information for diagnosis at the same time. We assume that the medical images from the same modality will exhibit similar statistics. This suggests that principal component analysis will be a good candidate for the block transform coding. And it will be unnecessary to store the principal eigenvectors for each images since this information can be calculated and stored in advance. Adaptive scheme will then be used to select proper basis for transform coding. Using this scheme, the peak signal to noise ratio can reach 49.81 dB with a compression ratio of 58.5.