In the first one, the input image is split into several bitplanes and each bitplane is encoded individually. We can divide these algorithms into two categories. The addressed compression algorithms are based on image decomposition, finite-context models, and arithmetic coding. In this chapter, we study the performance of several specific compression methods developed by the authors, as well as of image coding standards, when applied to medical images (computed radiography, computed tomography, magnetic resonance, and ultrasound), RNAi images, and microarray images. However, the original image can also be obtained, without any loss, if the decoding process goes until the end. This type of compression is usually designated as 'lossy-to-lossless compression.' These algorithms are very flexible, because they allow stopping decoding at a certain point, according to the available resources or user requirements. In the class of lossless compression algorithms, there is a specific type that has progressive decoding capabilities. In order to overcome this lower-performance issue, specific compression algorithms are essential. Usually, the performance of image coding standards, such as JPEG, JBIG, JPEG-LS, and JPEG2000, falls short when they are applied to certain types of biomedical images. On the other hand, if a given image is lossless compressed, it is possible to recompress it in the future, using a more efficient algorithm and without losing any information. On one hand, the use of lossless algorithms avoids problems of losing diagnostic information vital to identify life-threatening illnesses in the early stages. In fact, lossless methods are generally required in applications where cost, legal issues, and value play a decisive role, such as in medical imaging or in image archiving. Typically, lossless compression algorithms are recommended for dealing with these types of images. Those experiments involve marking cells with various fluorescent dyes to capture three components of interest, namely, DNA, actin, and PH3 channels. In order to overcome these problems, sophisticated compression methods are required.ДЄnother type of images that are addressed in this chapter are those that can be extracted from RNA interference (RNAi) experiments. Due to the development of these digital imaging technologies, some concerns appeared regarding efficient ways of storing and transmitting the images. Consequently, over 200 MB can be required to store a single microarray image. The output of a microarray experiment is a pair of 16-bits-per-pixel images, usually with a very high resolution, sometimes exceeding 13,000Г—4,000 pixels. These types of medical image modalities produce huge amounts of data that, for several reasons, need to be stored or transmitted with the highest possible fidelity between various hospitals and medical organizations.ДЄlso related to biomedical imaging, microarray images 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. The use of medical imaging has increased in the last years, especially with magnetic resonance imaging (MRI) and computed tomography (CT). The goal of an image compression method is to represent an arbitrary image using the smallest possible number of bits. It is fundamental in many different areas, such as biomedical imaging, consumer electronics, and Internet, among others. Image compression is a very important research field.

Construct 3 recompress images