title: Robust x-ray image segmentation by spectral clustering and active shape model.
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.
authors.affiliation: University of Tennessee , Mechanical Aerospace and Biomedical Engineering Department, 1506 Middle Drive, Knoxville, Tennessee 37996-2000, United States.
authors.forename: Jing
authors.initials: J
authors.lastname: Wu
authors.affiliation: University of Tennessee , Mechanical Aerospace and Biomedical Engineering Department, 1506 Middle Drive, Knoxville, Tennessee 37996-2000, United States.
authors.forename: Mohamed R
authors.initials: MR
authors.lastname: Mahfouz
keywords: active shape model; segmentation; spectral clustering; x-ray