Open Access

ARTICLE

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

Joddat Fatima^1,*, 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:

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

Received 09 December 2021; Accepted 25 January 2022; Issue published 25 May 2022

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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Keywords

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