Abstract
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.
Highlights
The segmentation of knee x-ray images has found wide applications in the analysis of anatomical structure and kinematics,[1,2,3,4] the assessment of loss of joint space width (JSW), the diagnosis of osteoarthritis (OA) and osteoporosis in terms of fracture detection and bone density measurement, and the planning for joint replacement.[5]
Our aim is to design a robust automatic segmentation method to extract the distal femur and proximal tibia contours from x-ray images
We present a robust segmentation method integrating spectral clustering and active shape model (ASM) to accurately extract bone contours in knee radiographs
Summary
The segmentation of knee x-ray images has found wide applications in the analysis of anatomical structure and kinematics,[1,2,3,4] the assessment of loss of joint space width (JSW), the diagnosis of osteoarthritis (OA) and osteoporosis in terms of fracture detection and bone density measurement, and the planning for joint replacement.[5] Manual segmentation of these anatomical structures is time consuming and subjective. Our aim is to design a robust automatic segmentation method to extract the distal femur and proximal tibia contours from x-ray images. Automatic segmentation is challenging due to the complex structure of the knee in x-ray images. The loss of key features of the femur contour may occur from overlapping neighboring bones, such as the tibia, and occlusion by implants or operational tools
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