Zhao Qian, Rosenbaum Kenneth, Okada Kazunori, Zand Dina J, Sze Raymond, Summar Marshall, Linguraru Marius George
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:3670-3. doi: 10.1109/EMBC.2013.6610339.
Down syndrome, the most common single cause of human birth defects, produces alterations in physical growth and mental retardation; its early detection is crucial. Children with Down syndrome generally have distinctive facial characteristics, which brings an opportunity for the computer-aided diagnosis of Down syndrome using photographs of patients. In this study, we propose a novel strategy based on machine learning techniques to detect Down syndrome automatically. A modified constrained local model is used to locate facial landmarks. Then geometric features and texture features based on local binary patterns are extracted around each landmark. Finally, Down syndrome is detected using a variety of classifiers. The best performance achieved 94.6% accuracy, 93.3% precision and 95.5% recall by using support vector machine with radial basis function kernel. The results indicate that our method could assist in Down syndrome screening effectively in a simple, non-invasive way.
唐氏综合征是人类出生缺陷最常见的单一病因，会导致身体发育异常和智力迟缓；其早期检测至关重要。唐氏综合征患儿通常具有独特的面部特征，这为利用患者照片进行唐氏综合征的计算机辅助诊断带来了契机。在本研究中，我们提出了一种基于机器学习技术的新颖策略来自动检测唐氏综合征。使用改进的约束局部模型来定位面部地标。然后在每个地标周围提取基于局部二值模式的几何特征和纹理特征。最后，使用多种分类器检测唐氏综合征。通过使用具有径向基函数核的支持向量机，最佳性能达到了94.6%的准确率、93.3%的精确率和95.5%的召回率。结果表明，我们的方法能够以简单、非侵入性的方式有效辅助唐氏综合征筛查。
