title: Nasopharyngeal carcinoma segmentation via HMRF-EM with maximum entropy.
abstract: This paper presents a novel automatic nasopharyngeal carcinoma segmentation approach used in magnetic resonance images. Adaptive calculation of the nasopharyngeal region location is first performed. The contour of the tumor is determined through distance regularized level set evolution with the initial contour obtained by the nearest neighbor graph model. To further refine the segmentation, a hidden Markov random field model with maximum entropy (HMRF-EM) is introduced to model the spatial information with prior knowledge. The proposed method is tested on magnetic resonance images of 26 nasopharyngeal carcinoma patients, and achieves good results.
authors.lastname: Kai-Wei Huang
authors.lastname: Zhe-Yi Zhao
authors.lastname: Qian Gong
authors.lastname: Juan Zha
authors.lastname: Liu Chen
authors.lastname: Ran Yang
mesh_terms: D000465:Algorithms; D002277:Carcinoma; D019277:Entropy; D006801:Humans; D008279:Magnetic Resonance Imaging; D000077274:Nasopharyngeal Carcinoma; D009303:Nasopharyngeal Neoplasms; D010363:Pattern Recognition, Automated