Abstract
In this paper we propose a fully automatic technique for Cobb angle computation from Scoliosis radiograph image where the objectives are to have no user intervention and to increase the reliability of spinal curvature magnitude quantification. The automatic technique mainly comprises of four steps, namely: Preprocessing, ROI identification, Object centerline extraction and Cobb angle computation from the extracted spine centerline. Bilateral image denoising is considered as the preprocessing step. Support Vector Machine classifier is used for object identification. We have assumed that the spine is a continuous contour rather than a series of discrete vertebral bodies with individual orientations. Morphological operation, Gaussian blurring, spine centerline approximation and polynomial fit are used to extract the centerline of spine. The tangent at every point of the extracted centerline is taken and Cobb angle is evaluated from these tangent values. To analyze the automated diagnosis technique, the proposed approach was evaluated on a set of 21 coronal radiograph images. Identification of ROI based on Support Vector Machine classifier is effective enough with a sensitivity and specificity of 100% and the center line extraction from this ROI gave correct results for 57.14% subjects with very less or negligible angular variability. As the vertebral endplates in radiograph images have poor contrast due to reduced radiation dose, the continuous contour based approach gives better reliability.
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Kundu, R., Chakrabarti, A., Lenka, P. (2018). Automated Cobb Angle Computation from Scoliosis Radiograph. In: Mandal, J., Sinha, D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_16
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DOI: https://doi.org/10.1007/978-981-13-1343-1_16
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