• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2019-02 (Vol.68 No.02)
  4. 10.5370/KIEE.2019.68.2.350
XML PDF INFO REF

References

1 
Kim Hee Eun, 2014, Review Article : Review on International Caries Detection and Assessment System, Journal of Korean Society of Dental Hygiene, Vol. 14, pp. 609-615Google Search
2 
Pitts N. B., Izmail A. I., Martignon S., Ekstrand K., Douglas G., Longbottom C., 2014, ICCMS™ guide for practitioners and educators, London: King’s College LondonGoogle Search
3 
Price J. B., 2013, A review of dental caries detection technologies, Academy of General dentistry, Program Approval for Continuing EducationGoogle Search
4 
Pretty I. A., 2006, Caries detection and diagnosis: novel technologies, Journal of dentistry, Vol. 34, No. , pp. 727-739DOI
5 
Seo Jeong Gwon, Kim Yeong Jae, Kim Gwang Gi, 2017. 08, Computer-Aided Diagnosis technology and AI, The Korean Institute of Electrical Engineers, Vol. 66, pp. 26-32Google Search
6 
Jatti A., Joshi R., 2017, Characterization of dental pathologies using digital panoramic X-ray images based on texture analysis, in Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, pp. 592-595DOI
7 
Bhan A., Goyal A., Chauhan N., Wang C. W., 2016, Feature Line Profile Based Automatic Detection of Dental Caries in Bitewing Radiography, in Micro- Electronics and Telecommunication Engineering (ICMETE), 2016 International Conference on, pp. 635-640DOI
8 
Singh P., Sehgal P., 2017, Automated caries detection based on Radon transformation and DCT, in Computing, Communication and Networking Technologies (ICCCNT), 2017 8th International Conference on, pp. 1-6DOI
9 
Ghaedi L., Gottlieb R., Sarrett D. C., Ismail A., Belle A., Najarian K., et al. , 2014, An automated dental caries detection and scoring system for optical images of tooth occlusal surface, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 1925-1928DOI
10 
Georgieva V. M., Mihaylova A. D., Petrov P. P., 2017, An application of dental X-ray image enhancement, in Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), 2017 13th International Conference on, pp. 447-450DOI
11 
Bhan A., Vyas G., Mishra S., Pandey P., 2016, Detection and Grading Severity of Caries in Dental X-ray Images, in Micro-Electronics and Telecommunication Engineering (ICMETE), 2016 International Conference on, pp. 375-378DOI
12 
Møystad A., Svanaes D., Van Der Stelt P., Grondahl H., Wenzel A., Van Ginkel F., et al. , 2003, Comparison of standard and task-specific enhancement of Digora® storage phosphor images for approximal caries diagnosis, Dentomaxillofacial Radiology, Vol. 32, pp. 390-396DOI
13 
Sornam M., Prabhakaran M., 2017, A new linear adaptive swarm intelligence approach using back propagation neural network for dental caries classification, in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 2698-2703DOI
14 
Kuang W., Ye W., 2008, A kernel-modified SVM based computer-aided diagnosis system in initial caries, in Second International Symposium on Intelligent Information Technology Application, pp. 207-211DOI
15 
Olsen G. F., Brilliant S. S., Primeaux D., Najarian K., 2009, An image-processing enabled dental caries detection system, in Complex Medical Engineering, 2009. CME. ICME International Conference on, pp. 1-8DOI
16 
Bampis C. G., Koutsouri G. D., Berdouses E., Tripoliti E. E., Iliopoulou D., Koutsouris D., et al. , 2014, Occlusal caries detection using random walker algorithm: A graph approach, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 1929-1932DOI
17 
Valizadeh S., Goodini M., Ehsani S., Mohseni H., Azimi F., Bakhshandeh H., 2015, Designing of a computer software for detection of approximal caries in posterior teeth, Iranian Journal of Radiology, Vol. 12DOI
18 
Tran M. T., Nguyen H. M., Nguyen V. T., Tran T. N., To H. N., 2016, Medical diagnosis from dental X-ray images: A novel approach using Clustering combined with Fuzzy Rule-based systems, in Fuzzy Information Processing Society (NAFIPS), 2016 Annual Conference of the North American, pp. 1-6DOI
19 
Datta S., Chaki N., 2015, Detection of dental caries lesion at early stage based on image analysis technique, in Computer Graphics, Vision and Information Security (CGVIS), 2015 IEEE International Conference on, pp. 89-93DOI
20 
Berdouses E. D., Koutsouri G. D., Tripoliti E. E., Matsopoulos G. K., Oulis C. J., Fotiadis D. I., 2015, A computer- aided automated methodology for the detection and classification of occlusal caries from photographic color images, Computers in biology and medicine, Vol. 62, pp. 119-135DOI
21 
Niroshika U., Meegama R., Fernando T., 2013, Active contour model to extract boundaries of teeth in dental X-ray images, in Computer Science & Education (ICCSE), 2013 8th International Conference on, pp. 396-401DOI
22 
Naam J., Harlan J., Madenda S., Wibowo E. P., 2016, Identification of the Proximal Caries of Dental X-Ray Image with Multiple Morphology Gradient Method, International Journal on Advanced Science, Engineering and Information Technology, Vol. 6, No. , pp. 345-348DOI
23 
Tikhe S. V., Naik A. M., Bhide S. D., Saravanan T., Kaliyamurthie K., 2016, Algorithm to identify enamel caries and interproximal caries using dental digital radiographs, in Advanced Computing (IACC), 2016 IEEE 6th International Conference on, pp. 225-228DOI
24 
ALbahbah A. A., El-Bakry H. M., Abd-Elgahany S., 2016, A New Optimized Approach for Detection of Caries in Panoramic Images, International Journal of Computer Engineering and Information Technology, Vol. 8, pp. 166Google Search
25 
Ali R. B., Ejbali R., Zaied M., 2016, Detection and classification of dental caries in x-ray images using deep neural networks, in Int. Conf. on Software Engineering Advances (ICSEA), pp. 236Google Search
26 
Kositbowornchai S., Siriteptawee S., Plermkamon S., Bureerat S., Chetchotsak D., 2006, An artificial neural network for detection of simulated dental caries, International Journal of Computer Assisted Radiology and Surgery, Vol. 1, pp. 91-96DOI
27 
Srivastava M. M., Kumar P., Pradhan L., Varadarajan S., 2017, Detection of Tooth caries in Bitewing Radiographs using Deep Learning, arXiv preprint arXiv:1711.07312Google Search
28 
C. Joonhyang, E. Hyunjun, K. Changick, 2015, Proximal Dental Caries Detection Using Convolutional Neural Network, pp. 374-377Google Search
29 
Lee J. H., Kim D. h., Jeong S. N., Choi S. H., 2018, Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm, Journal of periodontal & implant science, Vol. 48, No. , pp. 114-123DOI
30 
Lee J. H., Kim D. H., Jeong S. N., Choi S. H., 2018, Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm, Journal of dentistryDOI