Please use this identifier to cite or link to this item: https://doi.org/10.1049/cp:20080373
Title: Nasopharyngeal carcinoma lesion extraction using clustering via semi-supervised metric learning with side-information
Authors: Huang, W.
Kap, L.C.
Gao, Y.
Chong, V. 
Keywords: Clustering
Magnetic resonance images
Nasopharyngeal carcinoma lesion
Semi-supervised metric learning
Issue Date: 2008
Citation: Huang, W., Kap, L.C., Gao, Y., Chong, V. (2008). Nasopharyngeal carcinoma lesion extraction using clustering via semi-supervised metric learning with side-information. IET Conference Publications (543 CP) : 539-543. ScholarBank@NUS Repository. https://doi.org/10.1049/cp:20080373
Abstract: In this paper, we consider the extraction of nasopharyngeal carcinoma lesion from magnetic resonance images as a clustering problem. The metric used by the clustering algorithm in our proposed method is a new spatially weighted metric, which is learned by semi-supervised metric learning with side-information. Several experiments have been conducted to compare the performance of the proposed metric with similar metrics for the tumor extraction. ©2008 The Institution of Engineering and Technology.
Source Title: IET Conference Publications
URI: http://scholarbank.nus.edu.sg/handle/10635/132917
ISBN: 9780863419140
DOI: 10.1049/cp:20080373
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Dec 17, 2018

Page view(s)

16
checked on Nov 1, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.