
The accurate segmentation of the bone and articular cartilages from magnetic resonance (MR) images of the hip is important for clinical studies and drug trials into conditions like osteoarthritis. In current studies, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the hip cartilages, namely an approach to automatically segment the bones. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The accuracy and robustness of the approach was experimentally validated using an MR database of weVIBE, weDESS and MEDIC MR images. The (left, right) femoral and acetabular bone segmentation had a median Dice similarity coefficient of (0.921, 0.926) and (0.830, 0.813).
1707 Computer Vision and Pattern Recognition, segmentation, 610, bone, osteoarthritis, Cartilage, Segmentation, 616, Osteoarthritis, 1706 Computer Science Applications, cartilage, Bone
1707 Computer Vision and Pattern Recognition, segmentation, 610, bone, osteoarthritis, Cartilage, Segmentation, 616, Osteoarthritis, 1706 Computer Science Applications, cartilage, Bone
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