A Support Vector Machines Classifier to Assess the Severity of Idiopathic Scoliosis From Surface Topography

Ramirez, L.; Durdle, N.G.; Raso, V.J.; Hill, Doug

doi:10.1109/titb.2005.855526

Cited by 100 publications

(51 citation statements)

References 25 publications

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“…Similar approaches have already been tried, but they were either not further developed 55 or treated as raw input for further stages of analysis without any interpretation attempts. 58 Additionally, the chosen shape alignment method would have a great impact on this analysis as mentioned above.…”

Section: Discussionmentioning

confidence: 99%

Automatic recognition of surface landmarks of anatomical structures of back and posture

et al. 2012

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Abstract. Faulty postures, scoliosis and sagittal plane deformities should be detected as early as possible to apply preventive and treatment measures against major clinical consequences. To support documentation of the severity of deformity and diminish x-ray exposures, several solutions utilizing analysis of back surface topography data were introduced. A novel approach to automatic recognition and localization of anatomical landmarks of the human back is presented that may provide more repeatable results and speed up the whole procedure. The algorithm was designed as a two-step process involving a statistical model built upon expert knowledge and analysis of threedimensional back surface shape data. Voronoi diagram is used to connect mean geometric relations, which provide a first approximation of the positions, with surface curvature distribution, which further guides the recognition process and gives final locations of landmarks. Positions obtained using the developed algorithms are validated with respect to accuracy of manual landmark indication by experts. Preliminary validation proved that the landmarks were localized correctly, with accuracy depending mostly on the characteristics of a given structure. It was concluded that recognition should mainly take into account the shape of the back surface, putting as little emphasis on the statistical approximation as possible.

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Section: Discussionmentioning

confidence: 99%

Automatic recognition of surface landmarks of anatomical structures of back and posture

et al. 2012

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“…To our knowledge, studies on non invasive classification of scoliosis type or severity using machine learning methods are limited [17][18][19][20]. In the first work [17], an artificial neural network combined with genetic algorithm is used in order to estimate the Cobb angle.…”

Section: Introductionmentioning

confidence: 99%

“…However, the results are moderate with 70-82% of correct evaluation. In [19], the authors proposed a system where the subjects were classified into 3 severity groups (mild, moderate, severe) using 3D back shape image combined with other indicators like sex, age, etc. And, their system achieved 69-85% accuracy in testing.…”

Section: Introductionmentioning

confidence: 99%

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Adankon

Dansereau

Cheriet

2012

Artificial Intelligence in Medicine

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Non invasive classification system of scoliosis curve types using least-squares support vector machines." Artificial Intelligence in Medicine, Vol. 56, no. 2 (2012): pp. 99-107.The final publication is available via the DOI: http://dx.doi.org/10.1016/j.artmed.2012.07.002The manuscript, in a draft version prior to being accepted by the publisher, is reproduced in the following pages. Mathias AbstractObjective: To determine scoliosis curve types using non invasive acquisition, without any prior knowledge on X-ray data.Methods: Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of 3D back surface of the patients. The 3D image of back surface of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using principal component analysis and retain 53 components using overlap criterion combined with the total variance in the observed variables. In this work a multi-class classifier is built with leastsquares support vector machine (LS-SVM). The original LS-SVM formulation was modified by weighting differently the positive and negative samples and a new kernel was designed in order to achieve a robust classifier. The proposed system was validated using data of 165 patients with different scoliosis curve types. A comparison of the results of a non invasive classification was done with those obtained by an expert using X-ray images.Results: The average rate of successful classification was computed using * Corresponding author Email addresses: mathias-mahouzonsou.adankon@polymtl.ca (Mathias M. Adankon), jean.dansereau@polymtl.ca (Jean Dansereau), hubert.labelle@recherche-ste-justine.qc.ca (Hubert Labelle), farida.cheriet@polymtl.ca (Farida Cheriet) Preprint submitted to ElsevierJuly 16, 2012 leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. Considering the correct classification rate per class, we obtained 96%, 84% and 97% respectively for thoracic major curve, double major curve and lumbar/thoracolumbar major curve. Conclusion: This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods.The proposed system uses non invasive acquisition which is safety for the patient, no radiation. Also, a design of a specific kernel improved classification performance.

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“…However, the results are moderate with 70-82% of correct prediction. In (6), the authors proposed a prediction system where the subjects were classified into 3 severity groups (mild, moderate, severe) using 3D back shape image combined with other indicators like sex, age, etc. And, their system achieved 69-85% accuracy in testing.…”

Section: Introductionmentioning

confidence: 99%

Scoliosis curve type classification from 3D trunk image

et al. 2012

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Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.

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A Support Vector Machines Classifier to Assess the Severity of Idiopathic Scoliosis From Surface Topography

Cited by 100 publications

References 25 publications

Automatic recognition of surface landmarks of anatomical structures of back and posture

Automatic recognition of surface landmarks of anatomical structures of back and posture

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Scoliosis curve type classification from 3D trunk image

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