AN EFFECTIVE METHOD FOR CARIES DETECTION IN PANORAMIC DENTAL IMAGES
Keywords:
Tooth Decay, SVMs, Histograms of Oriented Gradients (HOG), Caries DetectionAbstract
Nowadays, support vector machines (SVMs) has been used broadly to solve numerous mind boggling issues in various fields because of its capacity to sum up and classify perfectly. One of these fields is the medicinal image preparing for diagnosing purposes. In this paper, tooth caries identification system is presented in light of SVMs that trained by using practical swarm optimization (PSO). The proposed approach utilizes inter-pixel autocorrelation as input features. Experimental results prove that the proposed approach can detect tooth caries efficiently. Furthermore, it is clear that the classification accuracy is very good. In addition, the proposed approach of tooth caries detection outperforms the diagnosing process performed by a rule-based computer assisted program and a group of dentists.
References
World Health Organization. Oral Health Fact Sheet. N318, April 2012. Availableonline: http://www.who.int/mediacentre/factsheets/fs318/en/. Accessed December 18, 2014.
Biomedical Photonics Handbook, Second Edition: Therapeutics and Advanced Biophotonics. Available online: https://www.crcpress.com/Biomedical-Photonics- Handbook-Second-Edition-Therapeutics-and- Advanced/Vo-Dinh/p/book/ 9781420085167. Accessed July 20, 2016
González-Cabezas C. The Chemistry of Caries: Remineralization and Demineralization Events with Direct Clinical Relevance. Dental Clinics. 2010 Jul 1;54(3): 469-78.
Oladele Tinuke O and Sanni Yetunde. Article: Dental Expert System. International Journal of Applied Information Systems 2015 January 8(2):1-15.
Quock RL. Dental Caries: A Current Understanding and Implications. Journal of Nature and Science. 2015 Jan 1;1(1):27..
Simón-Soro A, Mira A. Solving the etiology of dental caries. Trends in microbiology. 2015 Feb 28;23(2):76-82.
Genderson MW, Sischo L, Markowitz K, Fine D, Broder HL. An overview of children's oral health-related quality of life assessment: from scale development to measuring outcomes. Caries research. 2013 Oct 7;47(Suppl. 1):13- 21..
Kassem MA, Mekky NE, EL-Awady RM,”An Enhanced ATM Security System Using Multimodal Biometric Strategy”, International Journal of Electrical & Computer Sciences IJECS-IJENS 14 (4):9-16.
(Accuracy and Precision Online Source), Available: http://en.wikipedia.org/wiki/Accuracy_and_precision, Accessed on July 2014.
Oliveira J, Proença H. Caries detection in panoramic dental x-ray images. InComputational Vision and Medical Image Processing 2011 (pp. 175-190). Springer Netherlands.
R. Wang, Z. Hu, L. Chen, J. Xiong, "An Approach on Feature Selection of Cascaded Support Vector Machines with Particle Swarm Optimization Algorithm", Springer- Verlag Berlin Heidelberg, LNEE 140, Vol.2, (2012) .pp. 689–700.
Dalal N, Triggs B. Histograms of oriented gradients for human detection. In2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) 2005 Jun 25 (Vol. 1, pp. 886-893). IEEE.
Moynihan PJ, Kelly SA. Effect on caries of restricting sugars intake systematic review to inform WHO guidelines. Journal of dental research. 2013 Dec 9:0022034513508954.
Freeman WT, Tanaka KI, Ohta J, Kyuma K. Computer vision for computer games. InAutomatic Face and Gesture Recognition, 1996., Proceedings of the Second International Conference on 1996 Oct 14 (pp. 100-105). IEEE.
Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004 Nov 1;60(2):91-110.
Simon-Soro A, Belda-Ferre P, Cabrera-Rubio R, Alcaraz LD, Mira A. A tissue- dependent hypothesis of dental caries. Caries research. 2013 Sep 25;47(6):591- 600.
De Boer PT, Kroese DP, Mannor S, Rubinstein RY. A tutorial on the cross- entropy method. Annals of operations research. 2005 Feb 1;134(1):19-67.
Hazem M. El-Bakry, and Nikos Mastorakis, " An Effective Method for Detecting Dental Diseases by using Fast Neural Networks, " 8th WSEAS International Conference on SIGNAL, SPEECH AND IMAGE PROCESSING (SSIP '08), Santander, Cantabria, Spain, September 23-25, 2008, pp. 144-152.
Hazem M. El-Bakry, and Nikos Mastorakis, " A New Technique for Detecting Dental Diseases by using High Speed Neuro-Computers, " European computing Conf. (ECC '08), Malta, September 11-13, 2008, pp. 432-440.
Hazem M. El-Bakry, and Nikos Mastorakis, "A New Technique for Detecting Dental Diseases by using High Speed Artificial Neural Network," WSEAS Transactions on Computers, Issue 12, vol. 7, December 2008, pp. 1977- 1987.
Hazem M. El-Bakry, and Nikos Mastorakis, " An Effective Method for Detecting Dental Diseases by using Fast Neural Networks," WSEAS Transactions on Biology and Biomedicine, issue 11, vol. 5, November 2008, pp. 293-301.
Hazem M. El-Bakry, "Fast Virus Detection by using High Speed Time Delay Neural Networks," Journal of Computer Virology, vol.6, no.2, 2010, pp.115-122.
Hazem M. El-Bakry, "A Novel High Speed Neural Model for Fast Pattern Recognition," Soft Computing Journal, vol. 14, no. 6, 2010, pp. 647-666.
Hazem M. El-Bakry, "An Efficient Algorithm for Pattern Detection using Combined Classifiers and Data Fusion," Information Fusion Journal, vol. 11, issue 2, April 2010, pp. 133-148.
Hazem M. El-Bakry, "New Fast Principal Component Analysis For Real-Time Face Detection," MG&V Journal, vol. 18, no.4, 2009, pp. 405-426.
Hazem M. El-Bakry, "A New Neural Design for Faster Pattern Detection Using Cross Correlation and Matrix Decomposition," Neural Network World journal, 2009, vol. 19, no. 2, pp. 131-164.
Hazem M. El-Bakry, "New Faster Normalized Neural Networks for Sub-Matrix Detection using Cross Correlation in the Frequency Domain and Matrix Decomposition," Applied Soft Computing journal, vol. 8, issue 2, March 2008, pp. 1131-1149.
Hazem M. El-Bakry, and Nikos Mastorakis "New Fast Normalized Neural Networks for Pattern Detection," Image and Vision Computing Journal, vol. 25, issue 11, 2007, pp. 1767-1784.
Hazem M. El-Bakry, "New Fast Principal Component Analysis for Face Detection," Journal of Advanced Computational Intelligence and Intelligent Informatics, vol.11, no.2, 2007, pp. 195-201.
Ainas A. ALbahbah, Hazem M. El Bakry, and Sameh Abd-Elgahany, “Detection of Caries in Panoramic Dental X-ray Images using Back-Propagation Neural Networks," International Journal of Electronics Communication and Computer Engineering, vol. 7, issue 5, September 2016, pp. 250-256.
Thaer M. Taha, Hazem M. El-Bakry, Amal Ibrahim, Samir Abd-Elrazik, Magdi Z. Rashad “Fast sound verification using support vector machine and particle swarm optimization algorithms ,” International Journal of Advanced Research in Computer Science & Technology, vol. 4, issue 1, Jan.-March 2016, pp. 78- 83.
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