Segmentation of medical images through competitive learning

Atam P. Dhavan, Louis Arata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In image analysis applications, segmentation of gray-level images into meaningful regions is an important low-level processing step. Various approaches to segmentation investigated in the literature, in general, use either local information of gray-level values of pixels (region growing based methods, for example) or the global information (histogram thresholding based methods, for example). Application of these approaches for segmenting medical images with large structural information often does not provide satisfactory results. We present a novel approach to image segmentation that combines local contrast as well as global feature information. The presented method adaptively learns useful features and regions through the use of a normalized contrast function as a measure of local information and a competitive learning based method to update region segmentation incorporating global information about the gray-level distribution of the image. In this paper, we present the framework of such a self-organizing feature map, and show the results on simulated as well as real medical images.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Neural Networks
PublisherPubl by IEEE
Pages1277-1282
Number of pages6
ISBN (Print)0780312007
StatePublished - Jan 1 1993
Externally publishedYes
Event1993 IEEE International Conference on Neural Networks - San Francisco, California, USA
Duration: Mar 28 1993Apr 1 1993

Publication series

Name1993 IEEE International Conference on Neural Networks

Conference

Conference1993 IEEE International Conference on Neural Networks
CitySan Francisco, California, USA
Period3/28/934/1/93

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Control and Systems Engineering
  • Software
  • Artificial Intelligence

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