Optimized region finding and edge detection of knee cartilage surfaces from magnetic resonance images.
Expert hand-drawing of magnetic resonance image (MRI) features can be tedious and time consuming. MRI of the knee were acquired from eight subjects to develop an automated segmentation approach. The regions of interest (ROI) were femur, tibia, and patella cartilage. The Karhunen-Loeve transformation was used to construct prototypical ROI with accentuated features and reduced noise level. Adaptive template matching was then used to translate the prototypical ROI locations for detection and optimal overlap of ROI in test images. Cartilage boundaries at the optimal overlap area were computed based on standard gradient methods.
Angelini
Elsa D
ED
Departments of Biomedical Engineering, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
Ciaccio
Edward J
EJ
D002358:Cartilage, Articular / Q000473:pathology*; D005269:Femur / Q000473:pathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007719:Knee Joint / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015588:Observer Variation; D010003:Osteoarthritis / Q000175:diagnosis* / Q000473:pathology; D010329:Patella / Q000473:pathology; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011786:Quality Control; D013977:Tibia / Q000473:pathology