A computerized method for evaluating scoliotic deformities using elliptical pattern recognition in X-ray spine images. (July 2018)

• Record Type: Journal Article
• Authors: Pinheiro, Alan Petrônio; Coelho, Júlio Cézar; Veiga, Antônio C. Paschoarelli; Vrtovec, Tomaž
• Is Part Of: Computer methods and programs in biomedicine. Volume 161(2018)
• Page Start: 85
• Page End: 92
• Publication Date: 2018-07
• Subjects: Spine -- Cobb angle -- Computerized method -- Ellipse fitting -- Pattern recognition; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.04.015
• Languages: English
• ISSNs: 0169-2607

Abstract

Structured abstract: Background and Objective: Several studies have evaluated the reproducibility of the Cobb angle for measuring the degree of scoliotic deformities from X-ray spine images, and proposed different geometric models for describing the spinal curvature. The ellipse was shown to be an adequate geometric form, but was not yet applied for the identification and quantification of scoliotic curvatures. The purpose of this study is therefore to propose and validate a novel computerized methodology for the detection of elliptical patterns from X-ray images to evaluate the extent of the underlying scoliotic deformity. Methods: For anteroposterior each X-ray spine image, the spine curve is first reconstructed from vertebral centroids. The ellipse that best fits to the obtained spine curve is the found within a least square and genetic algorithm optimization framework. The geometric parameters of the resulting best fit ellipse are finally used to define an index that quantifies the spinal curvature. Results: The proposed methodology was validated on three synthetic images and then successfully applied to 20 clinical anteroposterior X-ray spine images of patients with a different degree of scoliotic deformity, with the resulting maximal relative error of 3% for the synthetic images and an overall error of 0.5 ± 0.4 mm (mean ± standard deviation) for the clinical cases. Conclusions: The results indicate that the proposed computerized methodology is able to reliably reproduce scoliotic curvatures using the geometric parameters of the underlying ellipses. In comparison to conventional approaches, the proposed methodology potentially produces less errors, requires a relatively low observer interaction, takes into account all vertebrae within the observed scoliotic deformity, and allows for both qualitative and quantitative evaluations that may complement the diagnosis, study and treatment of scoliosis.