Unified approach for multiple sclerosis lesion segmentation on brain MRI

Balasrinivasa Rao Sajja, Sushmita Datta, Renjie He, Meghana Mehta, Rakesh K. Gupta, Jerry S. Wolinsky, Ponnada A. Narayana

Research output: Contribution to journalArticlepeer-review

105 Scopus citations


The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)- and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations, and ratio maps of PD- and T2-weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field-expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under, and correct estimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low lesion load and 93% of the lesions in those cases with high lesion load.

Original languageEnglish (US)
Pages (from-to)142-151
Number of pages10
JournalAnnals of biomedical engineering
Issue number1
StatePublished - Jan 2006
Externally publishedYes


  • Expectation maximization
  • Feature classification
  • Hidden Markov random field
  • MRI
  • Multiple sclerosis
  • Segmentation

ASJC Scopus subject areas

  • Biomedical Engineering


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