Book ChapterDOI
Automated Cobb Angle Computation from Scoliosis Radiograph
TLDR
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.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.read more
Citations
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Use of the iPhone for Cobb angle measurement in scoliosis
TL;DR: The widespread availability of inclinometer-equipped mobile phones and the ability to store measurements in later versions of the angle measurement software may make these new technologies attractive for clinical measurement applications.
Journal ArticleDOI
A Novel Computer-Aided Method to Evaluate Scoliosis Curvature using Polynomial Math Function
D F Guamán-Lozada,J Cabrera-Escobar,M D Guamán-Lozada,V Romero-Rodríguez,A P Castro-Martin,M G Romero-Rodríguez,H Ying-Ying,Y Zhi-Han,HE Jiawei +8 more
TL;DR: It was concluded that CLT method is more reproducible than the Cobb method for measuring spinal curvature and more repeatable than Cobb Method.
References
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Journal ArticleDOI
Automatic Detection of Scoliotic Curves in Posteroanterior Radiographs
TL;DR: A new method for automatic detection of spinal curves from a PA radiograph is presented and the detected spinal curve is found to be statistically similar in 93% of cases to the manually identified curve.
Journal Article
Curve progression and spinal growth in brace treated idiopathic scoliosis.
TL;DR: The findings indicate the length of the spine measured on subsequent radiographs is an excellent parameter to determine spinal growth and thus an excellent predictor of scoliosis progression.
BookDOI
Support Vector Machines and Perceptrons
M. N. Murty,Rashmi Raghava +1 more
TL;DR: In this chapter, this chapter introduces some of the important terms associated with support vector machines and a brief history of their evolution.
Proceedings ArticleDOI
A Mask Based Segmentation Algorithm for Automatic Measurement of Cobb Angle from Scoliosis X-Ray Image
TL;DR: An X-ray image is accepted as input, Cobb angle is measured by the computer which is programmed to do so, thus eliminating the errors associated with the doctors interpretation, and the new methodology proposed is proposed.
Journal Article
Automatic Cobb Angle Measurement System by Using Nuclear Medicine Whole Body Bone Scan
TL;DR: A new automatic algorithm to measure Cobb angle by using nuclear medicine whole body bone scan images is presented, based on the fuzzy sets histogram thresholding, anatomical knowledge-based image segmentation method, and morphology technology.