9. Segmentation of Brain Magnetic Resonance Images

  1. Pradipta Maji1 and
  2. Sankar K. Pal2

Published Online: 17 FEB 2012

DOI: 10.1002/9781118119723.ch9

Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging

Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging

How to Cite

Maji, P. and Pal, S. K. (2012) Segmentation of Brain Magnetic Resonance Images, in Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118119723.ch9

Author Information

  1. 1

    Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

  2. 2

    Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India

Publication History

  1. Published Online: 17 FEB 2012
  2. Published Print: 27 JAN 2012

Book Series:

  1. Wiley Series on Bioinformatics: Computational Techniques and Engineering

Book Series Editors:

  1. Yi Pan and
  2. Albert Y. Zomaya

Series Editor Information

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

ISBN Information

Print ISBN: 9781118004401

Online ISBN: 9781118119723

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Keywords:

  • brain magnetic resonance (MR)image segmentation;
  • c-means algorithms;
  • pixel classification;
  • rough-fuzzy clustering algorithms

Summary

This chapter presents the application of different rough-fuzzy clustering algorithms for segmentation of brain magnetic resonance (MR) images. One of the important issues of the partitive-clustering-algorithm-based brain MR image segmentation method is the selection of initial prototypes of different classes or categories. The concept of discriminant analysis, based on the maximization of class separability, is used to circumvent the initialization and local minima problems of the partitive clustering algorithms. The chapter first deals with the pixel classification problem, and then gives an overview of the feature extraction techniques employed in segmentation of brain MR images, along with the initialization method of c-means algorithm based on the maximization of class separability. It presents implementation details, experimental results, and a comparison among different c-means algorithms. The algorithms compared are hard c-means (HCM), fuzzy c-means (FCM), possibilistic c-means (PCM), FPCM, rough c-means (RCM), and rough-fuzzy c-means (RFCM).

Controlled Vocabulary Terms

fuzzy set theory; image classification; image segmentation; magnetic resonance imaging; pattern clustering; rough set theory