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 0.48+ 0.13 mm for femur and 0.53+ 0.18 mm 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.
Journal of medical imaging