Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
Small Sample Size Learning for Shape Analysis of Anatomical Structures
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
An introduction to variable and feature selection
The Journal of Machine Learning Research
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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In this paper, we present a comprehensive framework to detect morphological changes in skull vaults of adolescent idiopathic scoliosis girls. To our knowledge, this is the first attempt to use a combination of medical knowledge, image analysis techniques, statistical learning tools, and scientific visualization methods to detect skull morphological changes. The shape analysis starts from a reliable 3-D segmentation of the skull using thresholding and math-morphological operations. The gradient vector flow is used to model the skull vault surface, which is followed by a spherically uniform sampling. The scale-normalized distances from the shape centroid to sample points are defined as the features. The most discriminative features are selected using recursive feature elimination for support vector machine. The results of this study specify the skull vault surface changes and shed light on building the evidence of bone formation abnormality in AIS girls.