Semi-supervised Nasopharyngeal Carcinoma Lesion Extraction from Magnetic Resonance Images Using Online Spectral Clustering with a Learned Metric

Authors:
Wei Huang;Kap Luk Chan;Yan Gao;Jiayin Zhou;Vincent Chong
Affiliations:
School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore;School of Medicine, National University of Singapore, Singapore;School of Medicine, National University of Singapore, Singapore
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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Abstract

In this paper, we consider the extraction of nasopharyngeal carcinoma lesion from MR images as a region segmentation problem. We propose a semi-supervised segmentation approach to segment the lesion in two steps. First, a metric is learned in a supervised fashion, which maximizes the separation between two groups of pixels (tumor or non-tumor) with minimal user interaction. Second, the learned metric is used to complete extraction of tumor region in an unsupervised fashion. Several experiments were conducted to evaluate the performance of similar methods with learned metrics for grouping or classifying pixels to form the tumor region. It is observed that the spectral clustering-based method performs well and the performance is comparable or marginally better than the discriminative SVM-based method.