28 February 2013 Down syndrome detection from facial photographs using machine learning techniques
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Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867003 (2013) https://doi.org/10.1117/12.2007267
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Down syndrome is the most commonly occurring chromosomal condition; one in every 691 babies in United States is born with it. Patients with Down syndrome have an increased risk for heart defects, respiratory and hearing problems and the early detection of the syndrome is fundamental for managing the disease. Clinically, facial appearance is an important indicator in diagnosing Down syndrome and it paves the way for computer-aided diagnosis based on facial image analysis. In this study, we propose a novel method to detect Down syndrome using photography for computer-assisted image-based facial dysmorphology. Geometric features based on facial anatomical landmarks, local texture features based on the Contourlet transform and local binary pattern are investigated to represent facial characteristics. Then a support vector machine classifier is used to discriminate normal and abnormal cases; accuracy, precision and recall are used to evaluate the method. The comparison among the geometric, local texture and combined features was performed using the leave-one-out validation. Our method achieved 97.92% accuracy with high precision and recall for the combined features; the detection results were higher than using only geometric or texture features. The promising results indicate that our method has the potential for automated assessment for Down syndrome from simple, noninvasive imaging data.
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Qian Zhao, Qian Zhao, Kenneth Rosenbaum, Kenneth Rosenbaum, Raymond Sze, Raymond Sze, Dina Zand, Dina Zand, Marshall Summar, Marshall Summar, Marius George Linguraru, Marius George Linguraru, } "Down syndrome detection from facial photographs using machine learning techniques", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867003 (28 February 2013); doi: 10.1117/12.2007267; https://doi.org/10.1117/12.2007267
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