Segmentation and quantification of black holes in multiple sclerosis.
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.
Datta
Sushmita
S
Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, 77030, USA.
Sajja
Balasrinivasa Rao
BR
He
Renjie
R
Wolinsky
Jerry S
JS
Gupta
Rakesh K
RK
Narayana
Ponnada A
PA
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