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Automatic segmentation of nasopharyngeal carcinoma from CT images: Region growing based technique

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4 Author(s)
Tatanun, C. ; Biomed. Eng. Programme, Mahidol Univ., Nakornpathom, Thailand ; Ritthipravat, P. ; Bhongmakapat, T. ; Tuntiyatorn, L.

This paper describes a framework for automatic nasopharyngeal carcinoma segmentation from CT images. The proposed technique is based on the Region Growing Method. It is automatic segmentation in which an initial seed is generated without human intervention. The seed is generated from a probabilistic map representing the chances of it being tumor. This map is created from three probabilistic functions based on location of the tumor, intensities, and non-tumor region respectively. The pixel in which the probability is the highest will be selected as potential seeds. Only one representative of these seeds will be selected as an initial seed. Then the seed will be used for region growing subsequently. The experimental results showed that the potential seeds and initial seed were correctly determined with a percentage accuracy of 81.60% and 95.10%. The seed was grown in preprocessed CT images for identifying the nasopharyngeal carcinoma region. The results showed that, perfect match and corresponding ratio were 71.31% and 53.00% respectively.

Published in:

Signal Processing Systems (ICSPS), 2010 2nd International Conference on  (Volume:2 )

Date of Conference:

5-7 July 2010

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