Prog Med Phys.  2015 Mar;26(1):52-58. 10.14316/pmp.2015.26.1.52.

Studies of Automatic Dental Cavity Detection System as an Auxiliary Tool for Diagnosis of Dental Caries in Digital X-ray Image

  • 1Medical Research Institute, Ewha Womans University, Seou, Korea.
  • 2Yonsei Institute of Convergence Technology, Yonsei University, Incheon, Korea.
  • 3Department of Dentistry, Ewha Womans University Medical Center, Seoul, Korea.
  • 4Department of Radiation Oncology, Ewha Womans University Medical Center, Seoul, Korea.


The automated dental cavity detection program for a new concept intra-oral dental x-ray imaging device, an auxiliary diagnosis system, which is able to assist a dentist to identify dental caries in an early stage and to make an accurate diagnosis, was to be developed. The primary theory of the automatic dental cavity detection program is divided into two algorithms; one is an image segmentation skill to discriminate between a dental cavity and a normal tooth and the other is a computational method to analyze feature of an tooth image and take an advantage of it for detection of dental cavities. In the present study, it is, first, evaluated how accurately the DRLSE (Direct Regularized Level Set Evolution) method extracts demarcation surrounding the dental cavity. In order to evaluate the ability of the developed algorithm to automatically detect dental cavities, 7 tooth phantoms from incisor to molar were fabricated which contained a various form of cavities. Then, dental cavities in the tooth phantom images were analyzed with the developed algorithm. Except for two cavities whose contours were identified partially, the contours of 12 cavities were correctly discriminated by the automated dental caries detection program, which, consequently, proved the practical feasibility of the automatic dental lesion detection algorithm. However, an efficient and enhanced algorithm is required for its application to the actual dental diagnosis since shapes or conditions of the dental caries are different between individuals and complicated. In the future, the automatic dental cavity detection system will be improved adding pattern recognition or machine learning based algorithm which can deal with information of tooth status.


Dental caries; Automatic dental cavity detection system; Level set methods; DRLSE

MeSH Terms

Machine Learning
Dental Caries*


  • Fig. 1. Seven tooth phantoms: all contain cavities inside.

  • Fig. 2. (a) Contour of the tooth. (b) Initial points in the cavities where the rectangular contour starts to evolve outside of the cavities.

  • Fig. 3. (a) Dental X-ray image, (b) Edge function by Gaussian smoothing, and (c) Edge function by coherent filter.

  • Fig. 4. The dental cavities of the 7 tooth phantoms are segmented from the 7 tooth phantoms after being applied edge-based active contour algorithm.



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