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Article
Abnormality Detection in Brain MR Images Using Minimum Error Thresholding Method
International Journal of Computational Intelligence and Applications
  • D. Selvathi, Mepco Schlcnk Engineering College
  • S. Thamarai Selvi, Anna University
  • Henry Selvaraj, University of Nevada, Las Vegas
Document Type
Article
Publication Date
6-1-2006
Abstract
Medical image segmentation plays an instrumental role in clinical diagnosis. An ideal medical image segmentation scheme should possess some preferred properties such as minimum user interaction, fast computation, and accurate and robust segmentation results. In this paper, an automated algorithm is proposed to enable the doctors to detect the presence of abnormal tissues in brain magnetic resonance images (MRIs). The merged image of different weighted images of each slice is obtained by averaging the intensities of pixels and is enhanced based on their local information by variance mapping. The abnormal regions are segmented by using minimum error thresholding method by formulating a criterion function. The segmentation is performed on the real data of MRI images for different abnormalities and the results are compared with radiologist labeled ground truth. Quantitative analysis between ground truth and segmented abnormal region is presented in terms of Percent Match and Correspondence Ratio. A maximum average percent match of 98.56% and correspondence ratio of 0.8892 of an MRI data is obtained.
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Citation Information
D. Selvathi, S. Thamarai Selvi and Henry Selvaraj. "Abnormality Detection in Brain MR Images Using Minimum Error Thresholding Method" International Journal of Computational Intelligence and Applications Vol. 6 Iss. 2 (2006) p. 177
Available at: http://works.bepress.com/henry_selvaraj/7/