Ministry of Health and Welfare , 2016, Korea Centers for Disease Control and Prevention, Korea Health Statistics 2015

Pretty I. A., Oh PK, 2006, Review-Caries detection and diagnosis : Novel technologies, in Journal Of Dentistry – Els evier, Vol. 34, pp. 727-739

Huh J., Nam H., Kim J., Park J., Shin S., Lee R., 2015, Studies of Automatic Dental Cavity Detection System as an Auxiliary Tool for Diagnosis of Dental Caries in Digital X-ray Image, Prog Med Phys, Vol. 26, pp. 52-58

Heo Chang-hoe, Kim Min-jeong, Cho Hyun-chong, 2016, A Study of Computer-aided Detection System for Dental Cavity on Digital X-ray Image, The transactions of The Korean Institute of Electrical Engineers, Vol. 65, No. 8, pp. 1424-1429

Pitts N. B., Ekstrand K. R., The ICDAS Foundation , 2013, International Caries Detection and Assessment System (ICDAS) and its International Caries Classification and Management System (ICCMS) – Methods for staging of the caries process and enabling dentists to manage caries, ommunity Dent. Oral. Epidemiol, Vol. 41, pp. 41-52

Canny J., 1987, A computational approach to edge detection, in Readings in Computer Vision, ed: Elsevier, pp. 184-203

Green B., 2002, Canny edge detection tutorial, Retrieved: March, Vol. 6

Kass M., Witkin A., Terzopoulos D., 1988, Snakes: Active contour models, International journal of computer vision, Vol. 1, pp. 321-331

OSHER Stanley, SETHIAN James A., 1988, Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations, Journal of computational physics, Vol. 79, No. 1, pp. 12-49

Chan T. F., Vese L. A., 2001, Active contours without edges, IEEE Transactions on image processing, Vol. 10, pp. 266-277

Li C., Xu C., Gui C., Fox M. D., 2010, Distance regularized level set evolution and its application to image segmentation, IEEE transactions on image processing, Vol. 19, pp. 3243-3254