Abstract
This paper presents a novel lossy compression scheme for medical images by using an incremental self–organized map (ISOM). Three neural networks for lossy compression scheme are comparatively examined: Kohonen map, multi-layer perceptron (MLP) and ISOM. In the compression process of the proposed method, the image is first decomposed into blocks of 8(8 pixels. Two-dimensional discrete cosine transform (2D-DCT) coefficients are computed for each block. The dimension of DCT coefficients vectors (codewords) is reduced by low-pass filtering. Huffman coding is applied to the indexes of codewords obtained by the ISOM. In the decompression process, inverse operations of each stage of the compression are performed in the opposite way. It is observed that the proposed method gives much better compression rates.
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© 2006 Springer-Verlag Berlin Heidelberg
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Dokur, Z. (2006). Compression of Medical Images by Using Artificial Neural Networks. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_37
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DOI: https://doi.org/10.1007/11816157_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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