I. J. of Electrical & Electronics Research
Research Article | 
Caries Detection from Dental Images using Novel Maximum Directional Pattern (MDP) and Deep Learning
Author(s) : A. Sherly Alphonse1, S. Vadhana Kumari2 and P. T. Priyanga3
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 2 , Special Issue on IEEE-SD
Publisher : FOREX Publication
Published : 13 May 2022
e-ISSN : 2347-470X
Page(s) : 100-104
Abstract
Various machine learning technologies and artificial intelligence techniques were applied on different applications of dentistry. Caries detection in orthodontics is a very much needed process. Computer-aided diagnosis (CAD) method is used to detect caries in dental radiographs. The feature extraction and classification are involved in the process of caries detection in dental images. In the 2D images the geometric feature extraction methods are applied and the features are extracted and then applied to machine learning algorithms for classification. Different feature extraction techniques can also be combined and then the fused features can be used for classification. Different classifiers support vector machine (SVM), deep learning, decision tree classifier (DT), Naïve Bayes (NB) classifier, k-nearest neighbor classifier (KNN) and random forest (RF) classifier can be used for the classification process. The proposed MDP extracts both intensity and edge information and creates the feature vector that increases the classification accuracy during caries detection.
Keywords: Caries, MDP, feature, machine, dental
A. Sherly Alphonse, Vellore Institute of Technology, Chennai, India; Email: sherly.a@vit.ac.in
S. Vadhana Kumari, Ilahia College of Engineering and Technology, Kerala, India
P. T. Priyanga, Ponjesly College of Engineering, Nagercoil, Tamilnadu, India
[1] Alphonse, A.S., Shankar, K., Jeyasheela Rakkini, M.J., Ananthakrishnan, S., Athisayamani, S., Robert Singh, A. and Gobi, R., 2021. A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification. Journal of Ambient Intelligence and Humanized Computing, 12(3), pp.3447-3463.[Cross Ref]
[2] Majanga V, Viriri S. Dental Images’ Segmentation Using Threshold Connected Component Analysis. Computational Intelligence and Neuroscience. 2021 Dec 14; 2021. [Cross Ref]
[3] Panetta K, Rajendran R, Ramesh A, Rao SP, Agaian S. Tufts Dental Database: A Multimodal Panoramic X-ray Dataset for Benchmarking Diagnostic Systems. IEEE Journal of Biomedical and Health Informatics. 2021 Oct 4.[Cross Ref]
[4] Mayur Rahul, Namita Tiwari, Rati Shukla, Devvrat Tyagi and Vikash Yadav (2022), A New Hybrid Approach for Efficient Emotion Recognition using Deep Learning. IJEER 10(1), 18-22. DOI: 10.37391/IJEER.100103.[Cross Ref]
[5] Ma Q, Kobayashi E, Fan B, Nakagawa K, Sakuma I, Masamune K, et al. Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery. Int J Med Robot. 2020; 16(3):e2093. https://doi.org/10.1002/rcs.2093 Epub 2020 Mar 20.[Cross Ref]
[6] Chung M, Lee M, Hong J, Park S, Lee J, Lee J, et al. Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation. Comput Biol Med. 2020; 120:103720. https://doi.org/10.1016/j.compbiomed.2020.103720 Epub 2020 Mar 28.[Cross Ref]
[7] Lee KS, Jung SK, Ryu JJ, Shin SW, Choi J. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. J Clin Med. 2020;9(2):392. https://doi.org/10.3390/jcm9020392 PMID: 32024114.
[8] J Dent Res. 2020; 99(3):249–56. https://doi.org/10.1177/0022034520901715 Epub 2020 Jan 24.[Cross Ref]
[9] Joni Hyttinen, Pauli Fält, Heli Jäsberg, Arja Kullaa, and Markku Hauta-Kasari,”Oral and Dental Spectral Image Database—ODSI-DB”, Applied Sciences 10(20), 7246 (2020).https://doi.org/10.3390/app10207246.
[10] Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific reports. 2019 Mar 7;9(1):1-1.[Cross Ref]
[11] Li P, Kong D, Tang T, Su D, Yang P, Wang H, Zhao Z, Liu Y. Orthodontic treatment planning based on artificial neural networks. Scientific reports. 2019 Feb 14;9(1):1-9.[Cross Ref]
[12] Faber J, Faber C, Faber P. Artificial intelligence in orthodontics. APOS Trends Orthod 2019;9(4):201-5.[Cross Ref]
[13] Sarabpreet Kaur, Jyoti Patel (2018), A Robust Image Mosaicing Technique Using Frequency Domain. IJEER 6(1), 1-8. DOI: 1037391/IJEER.060101. http://ijeer.forexjournal.co.in/archive/volume-6/ijeer-060101.php[Cross Ref]
[14] Alphonse AS, Dharma D. Novel directional patterns and a Generalized Supervised Dimension Reduction System (GSDRS) for facial emotion recognition. Multimedia Tools and Applications. 2018 Apr; 77(8):9455-88.[Cross Ref]
[15] Alphonse AS, Dharma D. A novel Monogenic Directional Pattern (MDP) and pseudo-Voigt kernel for facilitating the identification of facial emotions. Journal of Visual Communication and Image Representation. 2017 Nov 1; 49: 459-70.[Cross Ref]
[16] Hwang JJ, Lee JH, Han SS, Kim YH, Jeong HG, Choi YJ, et al. Strut analysis for osteoporosis detection model using dental panoramic radiography. Dentomaxillofac Radiol. 2017; 46(7):20170006. https://doi.org/10.1259/dmfr.20170006 Epub 2017 Jul 14.[Cross Ref]
[17] Lakhani K, Minocha B, Gugnani N. Analyzing edge detection techniques for feature extraction in dental radiographs. Perspectives in Science. 2016 Sep 1; 8:395-8.[Cross Ref]
[18] Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop. 2016; 149(1):127–33. https://doi.org/10.1016/j.ajodo.2015.07.030[Cross Ref]
[19] Wang X, Cai B, Cao Y, Zhou C, Yang L, Liu R, et al. Objective method for evaluating orthodontic treatment from the lay perspective: an eye-tracking study. Am J Orthod Dentofacial Orthop. 2016;150(4):601–10[Cross Ref]
[20] Aksakalli S, Demir A, Selek M, Tasdemir S. Temperature increase during orthodontic bonding with different curing units using an infrared camera. Acta Odontol Scand. 2014; 72(1):36–41. https://doi.org/10.3109/00016357.2013.794954 Epub 2013 May 3. PMID: 23638766.[Cross Ref]
[21] Tangel, M. L. et al. Dental classifcation for periapical radiograph based on multiple fuzzy attribute. In Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) 304–309 (IEEE, 2013). [Cross Ref]
[22] Rad, A. E., Rahim, M. S. M. & Norouzi, A. Digital Dental X-Ray Image Segmentation and Feature Extraction. Indonesian Journal of Electrical Engineering and Computer Science 11, 3109–3114 (2013). [Cross Ref]
[23] F. Shamsafar, "A new feature extraction method from dental X-ray images for human identification," 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), 2013, pp. 397-402, doi: 10.1109/IranianMVIP.2013.6780018.[Cross Ref]
[24] Mario MC, Abe JM, Ortega NR, Del Santo M Jr. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs. 2010; 34(7):E215–21. https://doi.org/10.1111/j.1525-1594.2010.00994.x Epub 2010 Jun 2.[Cross Ref]
[25] Shah, S., Abaza, A., Ross, A. & Ammar, H. Automatic Tooth Segmentation Using Active Contour Without Edges. In Biometric consortium conference, symposium 1–6 (IEEE, 2006). [Cross Ref]
[26] Said, E. H., Nassar, D. E. M., Fahmy, G. & Ammar, H. H. Teeth segmentation in digitized dental X-ray flms using mathematical morphology. IEEE Transactions on Information Forensics and Security 1, 178–189 (2006). [Cross Ref]
[27] Anjana Goen, D. C. Tiwari (2013), Pattern Recognition of Individual and Combined Fingers Movements Based Prosthesis Control Using Surface EMG Signals. IJEER 3(4), 70-78. DOI: 1037391/IJEER.030401. https://ijeer.forexjournal.co.in/archive/volume-3/ijeer-030401.php [Cross Ref]
[28] Mahoor, M. H. & Abdel-Mottaleb, M. Classifcation and numbering of teeth in dental bitewing images. Pattern Recognition 38, 577–586 (2005). [Cross Ref]
[29] Robinson GS. Edge detection by compass gradient masks. Computer graphics and image processing. 1977 Oct 1; 6(5):492-501. [Cross Ref]
A. Sherly Alphonse, S. Vadhana Kumari and P.T.Priyanga (2022), Caries Detection from Dental Images using Novel Maximum Directional Pattern (MDP) and Deep Learning. IJEER 10(2), 100-104. DOI: 10.37391/IJEER.100208.