Adaptive architecture neural nets for medical image compression

In this paper a technique is proposed for medical image compression using neural network based vector quantizers. There exist hundreds of modalities of medical images and each modality has hundreds of subclasses for different organs. In such a situation, it is difficult to generalize a neural network for all modalities. To tackle this problem and having a prior knowledge about similar nature of medical images for a single type, we propose a flag byte which is automatically set by image size and some other features. This flag byte is then used to select the size of the net and codebook, The proposed method not only leads to dynamic architectures of neural nets but also towards an adaptive selection of codebook sizes. This method yields high compression ratios with much better quality than existing standards