Open AccessOn the Importance of Location and Features for the Patch-Based Segmentation of Parotid Glands
Author(s) -
Christian Wachinger,
Matthew Brennan,
Greg Sharp,
Polina Golland
Publication year - 2014
Language(s) - English
DOI - 10.54294/vkfz56
Subject(s) - segmentation , computer science , artificial intelligence , pattern recognition (psychology) , entropy (arrow of time) , image segmentation , discriminator , image (mathematics) , k nearest neighbors algorithm , computer vision , telecommunications , physics , quantum mechanics , detector
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.