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Journal of Medical Imaging • Open Access

Robust x-ray image segmentation by spectral clustering and active shape model
Author(s): Jing Wu; Mohamed R. Mahfouz

Paper 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.

Paper Details

Date Published: 20 September 2016
PDF: 8 pages
J. Med. Img. 3(3) 034005 doi: 10.1117/1.JMI.3.3.034005
Published in: Journal of Medical Imaging Volume 3, Issue 3
Show Author Affiliations
Jing Wu, The Univ. of Tennessee Knoxville (United States)
Mohamed R. Mahfouz, The Univ. of Tennessee Knoxville (United States)


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