Abstract
Medical images form a vital part of a patient’s record in medical centers. Medical imaging devices generate data with huge memory requirements. Medical image compression is mandatory for storage and communication of medical data for the purpose of diagnosis. Compression removes the extraneous and redundant data in an image to reduce the storage cost as well as data transmission cost. Compression involves removing coding, interpixel or psychovisual redundancy in an image and, at the same time, retaining the integrity of the information required for the diagnosis in medical images. Lossless compression assures exact reconstruction of the original image after decompressing it and provides greater quality but lesser compression ratio. This paper presents two approaches for lossless compression of medical images. In the first approach, Huffman coding is implemented directly on medical images, whereas in the second approach, differential coding is applied on medical images before implementing Huffman coding. Experimental results show that differential Huffman coding improves the compression ratio.
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Singh, A., Khehra, B.S., Kohli, G.K. (2020). Differential Huffman Coding Approach for Lossless Compression of Medical Images. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (eds) Intelligent Computing and Communication. ICICC 2019. Advances in Intelligent Systems and Computing, vol 1034. Springer, Singapore. https://doi.org/10.1007/978-981-15-1084-7_56
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DOI: https://doi.org/10.1007/978-981-15-1084-7_56
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