Semi-supervised nasopharyngeal carcinoma lesion extraction from magnetic resonance images using online spectral clustering with a learned metric.
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.
Huang
Wei
W
School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore.
Chan
Kap Luk
KL
Gao
Yan
Y
Zhou
Jiayin
J
Chong
Vincent
V
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009303:Nasopharyngeal Neoplasms / Q000175:diagnosis*; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity