Active shape models—their training and application
Computer Vision and Image Understanding
Robust Active Shape Model Search
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Separating Style and Content with Bilinear Models
Neural Computation
A Stitching Algorithm for Automatic Registration of Digital Radiographs
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Automatic segmentation of femur bones in anterior-posterior pelvis x-ray images
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
Automatic extraction of femur contours from hip x-ray images
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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The growth of human bones forms a major problem when automatically segmenting orthopedic radiographs. Any template-based segmentation methods fails to fully capture these non-linear developments. However to extract orthopedic measurements or the bone age for patients of arbitrary age it is mandatory to have a segmentation scheme that deals with growth related changes. In this paper we propose a robust method based on Active Shape Models (ASMs) that on the one hand is invariant against the patient's age and on the other hand generalizes well over the large inter-patient variability. Our method achieves an accuracy of 0.48 mm for adult patients and 0.64 mm for children on a large test set of 180 images, with the patient's age covering a high range from less than one month to 93 years.