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Vertebra identification using template matching modelmp and \(K\)-means clustering

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement.

Methods

Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a \(K\)-means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment.

Results

The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5  % of the 330 tested cervical vertebrae.

Conclusions

An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.

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Acknowledgments

The authors acknowledge the Jolimont Hospital for providing the annotated radiographs used in this study. The authors would like to thank S. Drisis, physician in the imaging department at Jules Bordet Hospital, for his helpful advice on cervical spine trauma. The authors thank also the anonymous reviewers for their insightful comments that improved the quality of this paper.

Conflict of Interest

M. A. Larhmam, M. Benjelloun, and S. Mahmoudi declare that they have no conflict of interest.

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Authors and Affiliations

  1. Computer Science Department, Faculty of Engineering, University of Mons, Place du Parc, 20-7000 , Mons, Belgium

    Mohamed Amine Larhmam, Mohammed Benjelloun & Saïd Mahmoudi

Authors
  1. Mohamed Amine Larhmam
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  2. Mohammed Benjelloun
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  3. Saïd Mahmoudi
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Correspondence to Mohamed Amine Larhmam.

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Larhmam, M.A., Benjelloun, M. & Mahmoudi, S. Vertebra identification using template matching modelmp and \(K\)-means clustering. Int J CARS 9, 177–187 (2014). https://doi.org/10.1007/s11548-013-0927-2

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  • DOI: https://doi.org/10.1007/s11548-013-0927-2

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