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Hierarchical Constrained Local Model Using ICA and Its Application to Down Syndrome Detection
Qian Zhao1, Kazunori Okada2, Kenneth Rosenbaum3, Dina J. Zand3, Raymond Sze1, 4, Marshall Summar3, and Marius George Linguraru1
1Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Medical Center, Washington, DC, USA
2Computer Science Department, San Francisco State University, San Francisco, CA, USA
3Division of Genetics and Metabolism, Children’s National Medical Center, Washington, DC, USA
4Department of Radiology, Children’s National Medical Center, Washington, DC, USA
Abstract. Conventional statistical shape models use Principal Component Analysis (PCA) to describe shape variations. However, such a PCA-based model assumes a Gaussian distribution of data. A model with Independent Component Analysis (ICA) does not require the Gaussian assumption and can additionally describe the local shape variation. In this paper, we propose a Hierarchical Constrained Local Model (HCLM) using ICA. The first or coarse level of HCLM locates the full landmark set, while the second level refines a relevant landmark subset. We then apply the HCLM to Down syndrome detection from photographs of young pediatric patients. Down syndrome is the most common chromosomal condition and its early detection is crucial. After locating facial anatomical landmarks using HCLM, geometric and local texture features are extracted and selected. A variety of classifiers are evaluated to identify Down syndrome from a healthy population. The best performance achieved 95.6% accuracy using support vector machine with radial basis function kernel. The results show that the ICA-based HCLM outperformed both PCA-based CLM and ICA-based CLM.
Keywords: hierarchical constrained local model, independent component analysis, Down syndrome detection, classification
LNCS 8150, p. 222 ff.
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© Springer-Verlag Berlin Heidelberg 2013