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Extraction of bone contours from x-ray radiographs plays an important role in joint space width assessment, preoperative planning, and kinematics analysis. We present a robust segmentation method to accurately extract the distal femur and proximal tibia in knee radiographs of varying image quality. A spectral clustering method based on the eigensolution of an affinity matrix is utilized for x-ray image denoising. An active shape model-based segmentation method is employed for robust and accurate segmentation of the denoised x-ray images. The performance of the proposed method is evaluated with x-ray images from the public-use dataset(s), the osteoarthritis initiative, achieving a root mean square error of [Formula: see text] for femur and [Formula: see text] for tibia. The results demonstrate that this method outperforms previous segmentation methods in capturing anatomical shape variations, accounting for image quality differences and guiding accurate segmentation.

Robust x-ray image segmentation by spectral clustering and active shape model