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Compression of Medical Images by Using Artificial Neural Networks

  • Zümray Dokur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

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.

Keywords

Artificial Neural Network Mean Square Error Medical Image Ultrasound Image Image Compression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zümray Dokur
    • 1
  1. 1.Department of Electronics and Communication EngineeringIstanbul Technical UniversityMaslakTurkey

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