Publication:
Adaptive thresholding based on SOM technique for semi-automatic NPC image segmentation

Issued Date

2009-12-01

Resource Type

Conference Paper

DOI

10.1109/ICMLA.2009.135

Other identifier(s)

2-s2.0-77950841775

Rights

Mahidol University

Rights Holder(s)

SCOPUS

Bibliographic Citation

8th International Conference on Machine Learning and Applications, ICMLA 2009. (2009), 504-508

Suggested Citation

Weerayuth Chanapai, Panrasee Ritthipravat Adaptive thresholding based on SOM technique for semi-automatic NPC image segmentation. 8th International Conference on Machine Learning and Applications, ICMLA 2009. (2009), 504-508. doi:10.1109/ICMLA.2009.135 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/27456

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Title

Adaptive thresholding based on SOM technique for semi-automatic NPC image segmentation

Author(s)

Weerayuth Chanapai
 Panrasee Ritthipravat

Other Contributor(s)

Mahidol University

Abstract

This paper studies Self-Organizing Map (SOM) based adaptive thresholding technique for semi-automatic image segmentation. CT images of patients with nasopharyngeal carcinoma are considered in the study. The thresholds are determined from histogram of a topological map created from SOM method. With this proposed technique, initial tumor pixel must be manually selected. Pixels which are in the same threshold level are considered as tumor pixels. The experimental results showed that our proposed technique is effective for NPC image segmentation. In addition, it can properly handle tumor heterogeneity generally found in the NPC images. © 2009 IEEE.

Keyword(s)

Computer Science

Availability

URI

https://repository.li.mahidol.ac.th/handle/20.500.14594/27456

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Scopus 2006-2010