On the Importance of Location and Features for the Patch-Based Segmentation of Parotid Glands

Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3472
The segmentation of parotid glands in CT scans of patients with head and neck cancer is an essential part of treatment planning. We introduce a new method for the automatic segmentation of parotid glands that extends existing patch-based approaches in three ways: (1) we promote the use of image features in combination with patch intensity values to increase discrimination; (2) we work with larger search windows than established methods by using an approximate nearest neighbor search; and (3) we demonstrate that location information is a crucial discriminator and add it explicitly to the description. In our experiments, we compare a large number of features and introduce a new multi-scale descriptor. The best performance is achieved with entropy image features in combination with patches and location information.

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Categories: Atlas-based segmentation, Feature extraction, Image
Keywords: Atlas-based segmentation, Patches, Features, Approximate Nearest Neighbors
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