Pruned tree-structured vector quantization of medical images with segmentation and improved prediction
The authors use predictive pruned tree-structured vector quantization for the compression of medical images. Their goal is to obtain a high compression ratio without impairing the image quality, at least so far as diagnostic purposes are concerned. The authors use a priori knowledge of the class of images to be encoded to help them segment the images and thereby to reserve bits for diagnostically relevant areas. Moreover, the authors improve the quality of prediction and encoding in two additional ways: by increasing the memory of the predictor itself and by using ridge regression for prediction. The improved encoding scheme was tested via computer simulations on a set of mediastinal CT scans; results are compared with those obtained using a more conventional scheme proposed recently in the literature. There were remarkable improvements in both the prediction accuracy and the encoding quality, above and beyond what comes from the segmentation. Test images were encoded at 0.5 bit per pixel and less without any visible degradation for the diagnostically relevant region.