Abstract: This study provides an improved compression ratio over the existing compression algorithm of Huffman. The algorithm is tested on many kinds of files and is best suited for the Medical images like MRI, CTSCAN, and ULTRASOUND Medical images etc., which are mainly BMP images. Huffman Compression, also known as Huffman encoding, an algorithm for the lossless compression of files is based on the frequency of occurrence of a symbol in the file that is being compressed. It is a type of statistical coding, in which frequently occurring characters are given a short sequence while others that are used seldom get a longer bit sequence. The present system automates the whole process with the added advantage in the form of pattern recognition and footer information concept as a result of which an improved compression ratio over Huffman is achieved. The medical enterprise depends on a system that makes diagnostic images available for interpretation, that transmits images to physicians throughout the system, and that efficiently stores images pending retrieval for future medical or legal purposes. medical. Thus this study attempts to evaluate the performance of efficient and state of the art compression technique as applied to different types of medical images like CTScans, MRI, PET, Ultrasound, x-ray, Angiography Images etc We suggest a novel approach for compressing images or text documents based on building up a collection of n-length patterns in the image, and present the results of a network transfer implementation of this algorithm. It achieves better compression than existing alternate system (Huffman), and the decompression time appears substantially same as the previous method with almost the same compression rate and with no added complexity or resources. Thus, our Effective compression algorithm increases the compression ratio over Huffmann as well as maintains the quality of the Original image like Huffman.
Janet, J. and T.R. Natesan , 2005. Efective Compression Algorithm for Medical Images as an Aid to Telemedicine. Asian Journal of Information Technology, 4: 1180-1186.