Segmentation and quantification of black holes in multiple sclerosis

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

Research output: Contribution to journalArticle

61 Scopus citations

Abstract

A technique that involves minimal operator intervention was developed and implemented for identification and quantification of black holes on T1-weighted magnetic resonance images (T1 images) in multiple sclerosis (MS). Black holes were segmented on T1 images based on grayscale morphological operations. False classification of black holes was minimized by masking the segmented images with images obtained from the orthogonalization of T2-weighted and T1 images. Enhancing lesion voxels on postcontrast images were automatically identified and eliminated from being included in the black hole volume. Fuzzy connectivity was used for the delineation of black holes. The performance of this algorithm was quantitatively evaluated on 14 MS patients.

Original languageEnglish (US)
Pages (from-to)467-474
Number of pages8
JournalNeuroImage
Volume29
Issue number2
DOIs
StatePublished - Jan 15 2006

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Keywords

  • Black holes
  • Magnetic resonance imaging
  • Morphological grayscale reconstruction
  • Multiple sclerosis
  • T1 Hypointense lesions

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Datta, S., Sajja, B. R., He, R., Wolinsky, J. S., Gupta, R. K., & Narayana, P. A. (2006). Segmentation and quantification of black holes in multiple sclerosis. NeuroImage, 29(2), 467-474. https://doi.org/10.1016/j.neuroimage.2005.07.042