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

Jing Wu; Mohamed R. Mahfouz

doi:10.1117/1.JMI.3.3.034005

20 September 2016 Robust x-ray image segmentation by spectral clustering and active shape model

Jing Wu, Mohamed R. Mahfouz

Author Affiliations +

Journal of Medical Imaging, Vol. 3, Issue 3, 034005 (September 2016). https://doi.org/10.1117/1.JMI.3.3.034005

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.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Jing Wu and Mohamed R. Mahfouz "Robust x-ray image segmentation by spectral clustering and active shape model," Journal of Medical Imaging 3(3), 034005 (20 September 2016). https://doi.org/10.1117/1.JMI.3.3.034005

Published: 20 September 2016

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Cited by 29 scholarly publications.

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KEYWORDS

Image segmentation

X-rays

X-ray imaging

Bone

Image processing algorithms and systems

Image filtering

Gaussian filters

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