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An automatic lesion detection method for dental x-ray images by segmentation using variational level set

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3 Author(s)
Phen-Lan Lin ; Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan, ROC ; Po-Ying Huang ; Po-Whei Huang

Dental radiographs have been widely used by dentists in finding periodontal lesions or monitoring the progress of the periodontal defect treatment that is either impossible or difficult for human naked eyes. In this paper we propose a fully automatic gums lesion detection method for periapical dental X-ray images. The method includes two stages: (i) teeth- parts removing and (ii) lesion-region localization and severance labeling. In stage (i), morphological operations and histogram equalizations are first applied to enlarge the contrast between teeth and gums parts, then thresholding is used to separate the two types of regions. In stage (ii), gums-parts are first segmented into regions of normal, possible lesion or lesion, and serious lesion using a level set method with three coupled level set functions, and then the possible lesion or lesion region are further segmented into lesion and possible lesion regions using the same level set method. The experimental results demonstrate that our proposed method can detect and label all lesion regions in six periapical dental X-ray images which conform very well to human visual perception, and is robust to illumination variation to ± 30 intensity levels, as well.

Published in:

2012 International Conference on Machine Learning and Cybernetics  (Volume:5 )

Date of Conference:

15-17 July 2012