title: Segmentation and quantification of black holes in multiple sclerosis.
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.
authors.affiliation: Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, 77030, USA.
authors.forename: Sushmita
authors.initials: S
authors.lastname: Datta
authors.forename: Balasrinivasa Rao
authors.initials: BR
authors.lastname: Sajja
authors.forename: Renjie
authors.initials: R
authors.lastname: He
authors.forename: Jerry S
authors.initials: JS
authors.lastname: Wolinsky
authors.forename: Rakesh K
authors.initials: RK
authors.lastname: Gupta
authors.forename: Ponnada A
authors.initials: PA
authors.lastname: Narayana
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D005189:False Positive Reactions; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000473:pathology*; D015203:Reproducibility of Results