Segmentation of dual-echo MR images using neural networks

Jin Shin Chou*, Chin Tu Chen, Wei Chung Lin

*Corresponding author for this work

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

4 Scopus citations


We have integrated Kohonen's self-organizing feature maps with the idea of fuzzy sets and applied this model to the problem of dual-echo MR image segmentation. In the proposed method, a Kohonen network provides the basic structure and update rule, whereas fuzzy membership values control the learning rate. The calculation of learning rate is based on a fuzzy clustering algorithm. In the experiments, spatially registered T2-weighted and proton density MR data are used as input images. Every input image is first converted to a 1-D vector and two such vectors from two images are then combined to form a 2-D matrix. The initial weights are then fed into the model to start the iterative process. The process terminates when the stopping criteria is met. The major strength of the proposed approach is its stability and unsupervised nature. The experimental results show that the speed of convergence is faster than that of the fuzzy clustering method and the conventional region-based segmentation methods.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsMurray H. Loew
PublisherPubl by Society of Photo-Optical Instrumentation Engineers
Number of pages8
ISBN (Print)0819411310
StatePublished - Dec 1 1993
EventMedical Imaging 1993: Image Processing - Newport Beach, CA, USA
Duration: Feb 14 1992Feb 19 1992

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherMedical Imaging 1993: Image Processing
CityNewport Beach, CA, USA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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