Development of Algorithms for Medical Image Compression: Compression Algorithms

Development of Algorithms for Medical Image Compression: Compression Algorithms

Pandian R. (Sathyabama Institute of Science and Technology, India)
Copyright: © 2020 |Pages: 19
DOI: 10.4018/978-1-7998-0066-8.ch003
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Image compression algorithms are developed mainly for reduction of storage space, easier transmission, and reception. In this chapter, many image compression algorithms have been developed based on various combinations of transforms and encoding techniques. This research work mainly deals with the selection of optimum compression algorithms, suitable for medical images, based on the performance indices like PSNR and compression ratio. In order to find the effectiveness of the developed algorithms, characterization of the CT lung images are performed, before and after compression. The diagnosis of lung cancer is an important application for various medical imaging techniques. In this work, optimal texture features are identified for classification of lung cancer have also been incorporated as a case study. The texture features are extracted from the in CT lung images. BPN is trained to classify the features into normal and cancer.
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Basic Compression Techniques

Image compression can be accomplished by the use of coding methods, spatial domain techniques and transform domain techniques (Vemuri et al. 2007). Coding methods are directly applied to images. General coding methods comprise of entropy coding techniques which include Huffman coding and arithmetic coding, run length coding and dictionary-based coding. Spatial domain methods which operate directly on the pixels of the image combine spatial domain algorithms and coding methods. Transform domain methods transform the image from its spatial domain representation to a different type of representation using well-known transforms.

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