Image compression in medical image databases using set redundancy

Abstract

Summary form only given. Image compression is achieved by eliminating various types of redundancy that exist in the pixel values. Individual gray-scale images contain interpixel, psychovisual, and coding redundancy. However, sets of similar images contain an additional type of redundancy: the set redundancy. Set redundancy is the inter-image redundancy that results from the common information found in more than one image in the set. Set redundancy can be used to improve compression. Medical imaging is one of the best application areas for the enhanced compression model (ECM) and the set redundancy compression (SRC) methods. Medical images classified by modality and type of exam are very similar to one another, because of the standard procedures used in radiology. Therefore, medical image databases contain large amounts of set redundancy, which the ECM can efficiently reduce. Tests performed on a test database of CT brain scans showed significant compression improvement when the images were pre-processed with SRC methods to reduce set redundancy. The images were obtained from a random population of patients, and the tests were performed with the standard compression techniques used in radiology: Huffman encoding, arithmetic coding, and Lempel-Ziv compression. The best improvement resulted from combining the min-max predictive method with Huffman compression. In our tests we used genetic algorithms to identify the sets of similar images in the image database.