A unified approach for lesion segmentation on MRI of multiple sclerosis

B. R. Sajja, S. Datta, R. He, P. A. Narayana

Research output: Contribution to journalConference article

12 Scopus citations


Accurate determination of lesion volumes on brain MR images is hampered by the presence of a large number of false positive and negative classifications. A strategy that combines parametric and nonparametric techniques is developed and implemented for minimizing the false classifications. Initially, CSF and lesions are segmented using Parzen window classifier. Image processing, morphological operations, and ratio map of proton density (PD) and T2 weighted images are used for minimizing false positives. Lesions are delineated using fuzzy connectedness principle. Contextual information was used for minimizing false negative lesion classifications. Gray and white matter classification is realized using HMRF-EM algorithm.

Original languageEnglish (US)
Pages (from-to)1778-1781
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 III
StatePublished - Dec 1 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004



  • Feature classification
  • MRI
  • Morphological operators
  • Multiple sclerosis
  • Segmentation

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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