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Automatic Localization and Segmentation of Vertebrae for Cobb Estimation and Curvature Deformity

Joddat Fatima1,*, Amina Jameel2, Muhammad Usman Akram3, Adeel Muzaffar Syed1, Malaika Mushtaq3

1 Bahria University, Islamabad, 44000, Pakistan
2 Bahria University, Karachi, 74800, Pakistan
3 National University of Sciences and Technology, Islamabad, 44000, Pakistan

* Corresponding Author: Joddat Fatima. Email: email

Intelligent Automation & Soft Computing 2022, 34(3), 1489-1504. https://doi.org/10.32604/iasc.2022.025935

Abstract

The long twisted fragile tube, termed as spinal cord, can be named as the second vital organ of Central Nervous System (CNS), after brain. In human anatomy, all crucial life activities are controlled by CNS. The spinal cord does not only control the flow of information from the brain to rest of the body, but also takes charge of our reflexes control and the mobility of body. It keeps the body upright and acts as the main support for the flesh and bones. Spine deformity can occur by birth, due to aging, injury or spine surgery. In this research article, we have proposed a new three step framework for analysis of spine deformity where we have introduced vertebrae segmentation as object localization problem. You Only Look Once (YOLO) is utilized for localization of vertebrae, which achieved the mAP of 97.5% for Mendeley dataset and 95.2% for Computational methods and clinical applications for Spine Imaging (CSI) 2016 dataset. In the second step, edge detection, is done by Holistic Edge Detection (HED) and for corner calculation, the Harris method is used. In the final step we calculated the Cobb angle for the deformity analysis. Mean Absolute Error (MAE) is calculated that was found to be less than 0.40° for Mendeley and 0.50° for CSI 2016 dataset. The classification of Lumbar Lordosis with corner point Cobb estimation method achieved an accuracy up to 98.04% for the Mendeley dataset and 81.25% for CSI 2016 dataset respectively. A comparative analysis is done for Cobb estimation and the results showed that the proposed framework has reduced mean error up to 2 degree.

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Cite This Article

APA Style
Fatima, J., Jameel, A., Akram, M.U., Syed, A.M., Mushtaq, M. (2022). Automatic localization and segmentation of vertebrae for cobb estimation and curvature deformity. Intelligent Automation & Soft Computing, 34(3), 1489-1504. https://doi.org/10.32604/iasc.2022.025935
Vancouver Style
Fatima J, Jameel A, Akram MU, Syed AM, Mushtaq M. Automatic localization and segmentation of vertebrae for cobb estimation and curvature deformity. Intell Automat Soft Comput . 2022;34(3):1489-1504 https://doi.org/10.32604/iasc.2022.025935
IEEE Style
J. Fatima, A. Jameel, M.U. Akram, A.M. Syed, and M. Mushtaq "Automatic Localization and Segmentation of Vertebrae for Cobb Estimation and Curvature Deformity," Intell. Automat. Soft Comput. , vol. 34, no. 3, pp. 1489-1504. 2022. https://doi.org/10.32604/iasc.2022.025935



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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