Automatic Approach for Detection of Abnormality within MRI Dental Images Using Gaussian Filtering and SVM Hybridization

Authors(2) :-Sharanjit Kaur, Navjot Kaur

Dental care is state of the art problem which must be detected at early stage in order to overcome severities. To tackle the problem proposed system uses multiple techniques present within image processing and hybridized it. Proposed system uses pre-processing is performed to improve quality of images using modified Gaussian filtering, feature extraction process is performed in order to determine critical and non-critical segments, morphological filtering mechanism is applied to determine the teeth ends to be classified properly and support vector machine is applied to classify the disease to appropriate class. Results are obtained in terms of classification accuracy and mean square error. Overall simulation is conducted within MATLAB 2017 and result is 99% in terms of accuracy.

Authors and Affiliations

Sharanjit Kaur
Computer Science, GIMET, Amritsar, Punjab, India
Navjot Kaur
Computer Science, GIMET, Amritsar, Punjab, India

Classification accuracy, Dental Caries, Image Processing, Mean square error, Pre-processing, SVM.

  1. K. Taylor-Weetman, “Comparison Of Panoramic And Image Representation Of Radiographyfor The Detection Of Dental Caries?: A Sysyematic Review Of Diagnostic Tests .,” No. June 2002.
  2. H. Baseri, R. Rafeh, S. T. F, M. Houshyar, and L. Khojastepour, “Introducing a Dental Caries Marking Software and Evaluate Radiologists ’ Disagreement in Caries Detection Using this Software,” vol. 2, no. 1, pp. 10–17, 2015.
  3. P. Ribeiro, “Caries Detection in Panoramic Dental X-ray Images Caries Detection in Panoramic Dental X-ray Images,” no. August, 2009.
  4. D. T. Zero, M. Fontana, M. Ando, and S. Bayne, “The biology, prevention, diagnosis and treatment of dental caries,” vol. 140, no. September, pp. 25–34, 2009.
  5. A. J. Solanki, “ISEF Based Identification of RCT / Filling in Dental Caries of Decayed Tooth,” no. 7, pp. 149–162, 2013.
  6. P. E. Petersen, R. J. Baez, and M. A. Lennon, “Advances in Dental Research Community-oriented Administration of Fluoride for the Prevention of Dental Caries?: A Summary of the Current Situation in Asia,” 2012.
  7. S. Needs and A. Programme, “Dental Caries in Children Dental Caries in Children.”
  8. B. Radiography, “Comparison of Visual Examination , Bite-wing Radiography , and Fiberoptic Transillumination on Caries Detection,” vol. 7, no. August, pp. 77–81, 2017.
  9. I. A. Pretty, “Caries detection and diagnosis?: Novel technologies,” vol. 34, pp. 727–739, 2006.
  10. I. A. Pretty, “A Closer Look at Diagnosis in Clinical Dental Practice?: Part 5 . Emerging Technologies.”
  11. F. Shakibaie, R. George, and L. J. Walsh, “Applications of Laser induced Fluorescence in Dentistry,” vol. 3, no. 3, pp. 38–44, 2011.
  12. M. Naebi, E. Saberi, S. R. Fakour, A. Naebi, S. H. Tabatabaei, S. A. Moghadam, E. Bozorgmehr, N. D. Behnam, and H. Azimi, “Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm,” vol. 2016, 2016.
  13. G. D. Koutsouri, E. Berdouses, E. E. Tripoliti, C. Oulis, D. I. Fotiadis, and S. Member, “Detection of occlusal caries based on digital image processing,” pp. 1–4, 2013.
  14. P. L. Lin, P. W. Huang, Y. S. Cho, and C. H. Kuo, “An automatic and effective tooth isolation method for dental radiographs,” vol. 21, no. 1, pp. 126–136, 2013.
  15. “An Effective Shape Extraction Algorithm for Dental Radiographs using Contour Information,” vol. 8491, pp. 311–316, 2011.
  16. W. G. M. Geraets, C. Lindh, and H. Verheij, “Sparseness of the trabecular pattern on dental radiographs?: visual assessment compared with semi-automated measurements,” vol. 85, no. August, pp. 455–460, 2012.
  17. V. B. Kumar, “Dermatological Disease Detection Using Image Processing and Machine Learning,” IEEE, pp. 88–93, 2016.
  18. D. J. Cook, J. C. Augusto, and V. R. Jakkula, “Ambient intelligence?: Technologies , applications , and opportunities,” Pervasive Mob. Comput., vol. 5, no. 4, pp. 277–298, 2009.
  19. D. Selvaraj, “MRI BRAIN IMAGE SEGMENTATION TECHNIQUES- A REVIEW ,”vol.4, no.5, pp.364-381,2013.
  20. P. Pandey, A. Bhan, M. K. Dutta, and C. M. Travieso, “Automatic Image Processing Based Dental Image Analysis Using Automatic Gaussian Fitting Energy and Level Sets,” IEEE ACCESS, 2017.
  21. P. L. Lin, P. Y. Huang, P. W. Huang, H. C. Hsu, and C. C. Chen, “Teeth segmentation of dental periapical radiographs based on local singularity analysis,” Comput. Methods Programs Biomed., pp. 1–13, 2013.
  22. Meenakshi Sharma , Dr.Himanshu Aggarwal, "Evaluation factors for testing and validation of Clinical Reporting System" , International Journal of Computer Science and Engineering,vol.6,issue-2,e-ISSN:2347-2693 on 2018.
  23. S. Chang, M. Siao, T. Lai, B. Ibragimov, and T. Vrtovec, “A benchmark for comparison of dental radiography analysis,” vol. 31, pp. 63–76, 2016.
  24. Nazemi, A. & Maleki, A., 2014. Artificial neural network classifier in comparison with LDA and LS-SVM classifiers to recognize 52 hand postures and movements. Proceedings of the 4th International Conference on Computer and Knowledge Engineering, ICCKE 2014, pp.18–22.
  25. Meenakshi Sharma, Dr. Himanshu Aggarwal, "Mobile based application for prediction of diabetes mellitus: FHIR Standard" , International Journal of Engineering and technology,vol.7,issue-2,e-ISSN:3347-3696 on 2018.
  26. Ramya, V., 2018. SVM Based Detection for Diabetic Retinopathy. IEEE, V(I), pp.11–13.
  27. Satone, M. & Kharate, G., 2014. Feature Selection Using Genetic Algorithm for Face Recognition Based on PCA , Wavelet and SVM. IEEE, 6(1), pp.39–52.
  28. Daliman, S., Rahman, S.A. & Busu, I., 2014. Segmentation of Oil Palm Area Based on GLCM- SVM and NDVI. IEEE, pp.645–650.
  29. Meenakshi Sharma and Dr. Himanshu Aggarwal, "Methodologies of legacy clinical decision support system - A review" International Conference on Recent Innovations in Computer Science and Information Technology.2017 May. 
  30. Farooq, M.A., Azhar, M.A.M. & Raza, R.H., 2016. Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers. 2016 IEEE 16th International Conference on Bioinformatics

Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 326-334
Manuscript Number : CSEIT183571
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sharanjit Kaur, Navjot Kaur, "Automatic Approach for Detection of Abnormality within MRI Dental Images Using Gaussian Filtering and SVM Hybridization", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.326-334, May-June-2018.
Journal URL :

Article Preview