Lossy-to-Lossless Compression of Biomedical Images Based on Image Decomposition

The use of medical imaging has increased in the last years, especially with magnetic resonance imaging (MRI) and computed tomography (CT). Microarray imaging and images that can be extracted from RNA interference (RNAi) experiments also play an important role for large-scale gene sequence and gene expression analysis, allowing the study of gene function, regulation, and interaction across a large number of genes and even across an entire genome. These types of medical image modalities produce huge amounts of data that, for several reasons, need to be stored or transmitted at the highest possible fidelity between various hospitals, medical organizations, or research units. In this chapter, we study the performance of several compression methods developed by the authors, as well as of image coding standards, when used to compress medical images (computed radiography, computed tomography, magnetic resonance, and ul‐ trasound), RNAi images, and microarray images. The compression algorithms ad‐ dressed are based on image decomposition, finite-context modeling, and arithmetic coding. In one of the methods, the input image is split into several bitplanes, and each bitplane is encoded using finite-context models and arithmetic coding. In another ap‐ proach, the intensity levels of a given image are organized in a binary-tree structure, where each leaf node is associated with an image intensity. The experimental results presented in this chapter are state of the art regarding the compression of some of these types of images. Moreover, several approaches and pre‐ processing techniques are presented, giving a good hint about new developments that can be studied further. Also, this chapter intends to be used as a reference for compar‐ ison with new compression algorithms that may be developed in the future.