NDLI: An effective teeth segmentation method for dental periapical radiographs based on local singularity

Content Provider	IEEE Xplore Digital Library
Author	Lin, P.L. Huang, P.Y. Huang, P.W.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Providence Univ., Taichung, Taiwan (Lin, P.L.) \|\| Dept. of Comput. Sci. & Eng., Nat. Chung Hsing Univ., Taichung, Taiwan (Huang, P.Y.; Huang, P.W.)
Abstract	Dental radiographs play an important role for dental diagnosis, as most anomalies are hidden under the surface and cannot be seen during a visual examination. For effective computer-aided dental diagnosis, accurate teeth segmentation is one of the most critical tasks, because cysts and inflammatory lesions usually occur around tooth periapical (tooth-roots) areas and these areas in radiographs are often subject to noise, low contrast, and uneven illumination. In this paper, we propose an effective scheme to segment each tooth in dental periapical radiographs based on local singularity analysis. At first, a proposed adaptive power law transformation is applied to reduce variations of contrasts between teeth and alveolar bones (gums). Then local singularities measured by Hölder exponent are computed to obtain a structure image in which the structures of teeth are much smoother than the structures of gums. Otsu's thresholding is applied to segment teeth from gums and finally, connected component analysis and morphological operations are applied to isolate each tooth. Experimental results demonstrate that out of 18 teeth in six tested periapical images, all teeth are successfully segmented with 17 extracted tooth-contours almost completely conforming to human visual perception.
Starting Page	407
Ending Page	411
File Size	1061158
Page Count	5
File Format	PDF
ISBN	9781479900077
ISSN	23250925
e-ISBN	9781479900091
DOI	10.1109/ICSSE.2013.6614700
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-04
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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