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.
A hybrid multi-scale approach to automatic airway tree segmentation from CT scans
