Learning active shape models for bifurcating contours

TY - JOUR

T1 - Learning active shape models for bifurcating contours

AU - Seise, M.

AU - McKenna, S. J.

AU - Ricketts, I. W.

AU - Wigderowitz, C. A.

N1 - dc.publisher: Institute of Electrical and Electronics Engineers

PY - 2007/5

Y1 - 2007/5

N2 - Statistical shape models are often learned from examples based on landmark correspondences between annotated examples. A method is proposed for learning such models from contours with inconsistent bifurcations and loops. Automatic segmentation of tibial and femoral contours in knee X-ray images is investigated as a step towards reliable, quantitative radiographic analysis of osteoarthritis for diagnosis and assessment of progression. Results are presented using various features, the Mahalanobis distance, distance weighted K-nearest neighbours, and two relevance vector machine-based methods as quality of fit measure

AB - Statistical shape models are often learned from examples based on landmark correspondences between annotated examples. A method is proposed for learning such models from contours with inconsistent bifurcations and loops. Automatic segmentation of tibial and femoral contours in knee X-ray images is investigated as a step towards reliable, quantitative radiographic analysis of osteoarthritis for diagnosis and assessment of progression. Results are presented using various features, the Mahalanobis distance, distance weighted K-nearest neighbours, and two relevance vector machine-based methods as quality of fit measure

KW - Active shape models

KW - Image segmentation

KW - Image shape analysis

KW - Osteoarthritis

KW - X-ray imaging

UR - https://www.scopus.com/pages/publications/34247554484

U2 - 10.1109/TMI.2007.895479

DO - 10.1109/TMI.2007.895479

M3 - Article

C2 - 17518061

SN - 0278-0062

VL - 26

SP - 666

EP - 677

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

IS - 5

ER -