Siemens Medical Solutions Usa, Inc.

Zhao Yi

Yiqiang Zhan

Maneesh Dewan

Xiang Sean Zhou

Gerardo Hermosillo Valadez

Described herein is a technology for facilitating deformable model-based segmentation of image data. In one implementation, the technology includes receiving training image data () and automatically constructing a hierarchical structure () based on the training image data. At least one spatially adaptive boundary detector is learned based on a node of the hierarchical structure ().

Hierarchical deformable model for image segmentation

Described herein is a framework for constructing a hierarchical classifier for facilitating classification of digitized data. In one implementation, a divergence measure of a node of the hierarchical classifier is determined. Data at the node is divided into at least two child nodes based on a splitting criterion to form at least a portion of the hierarchical classifier. The splitting criterion is selected based on the divergence measure. If the divergence measure is less than a predetermined threshold value, the splitting criterion comprises a divergence-based splitting criterion which maximizes subsequent divergence after a split. Otherwise, the splitting criterion comprises an information-based splitting criterion which seeks to minimize subsequent misclassification error after the split.

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Pasadena, CA, United States of America

Zhao Yi