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A multiscale approach to automatic medical image segmentation using self-organizing map

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Abstract

In this paper, a new medical image classification scheme is proposed using self-organizing map (SOM) combined with multiscale technique. It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers. First, to solve the difficulty in manual selection of edge pixels, a multiscale edge detection algorithm based on wavelet transform is proposed. Edge pixels detected are then selected into the training set as a new class and a multiscale SOM classifier is trained using this training set. In this new scheme, the SOM classifier can perform both the classification on the entire image and the edge detection simultaneously. On the other hand, the misclassification of the traditional multiscale SOM classifier in regions near edges is greatly reduced and the correct classification is improved at the same time.

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Authors and Affiliations

  1. Department of Automation, Tsinghua University, 100084, Beijing, P.R. China

    Ma Feng & Xia Shaowei

Authors
  1. Ma Feng
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  2. Xia Shaowei
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Additional information

This project is supported by the National Natural Science Foundation (No. 69775001) and the National Hi-Tech Program (863-05-18) of China.

Ma Feng is a Ph.D. candidate in the Department of Automation, Tsinghua University, P.R. China. He received his B.S. and M.S. degrees of engineering from the Department of Automation, Tsinghua University in 1993 and 1996 respectively. His research interests are image processing especially medical image segmentation, pattern recognition, neural network, multiscale techniques including wavelet theory and scale-space theory.

Xia Shaowei is a Professor in the Department of Automation, Tsinghua University. Her research areas are system engineering, neural network and pattern recognition.

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Ma, F., Xia, S. A multiscale approach to automatic medical image segmentation using self-organizing map. J. of Comput. Sci. & Technol. 13, 402–409 (1998). https://doi.org/10.1007/BF02948498

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  • DOI: https://doi.org/10.1007/BF02948498

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