Document Type : Original Research
Authors
1 Department of Oral & Maxillofacial Radiology, school of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
2 Department of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
Abstract
Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment.Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis.Results: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity.Conclusion: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.
Keywords
- Alkhader M, Ohbayashi N, Tetsumura A, Nakamura S, Okochi K, Momin MA, et al. Diagnostic performance of magnetic resonance imaging for detecting osseous abnormalities of the temporomandibular joint and its correlation with cone beam computed tomography. Dentomaxillofac Radiol. 2010;39:270-6. doi.org/10.1259/dmfr/25151578. PubMed PMID: 20587650. PubMed PMCID: 3520245.
- Okeson JP, de Kanter RJ. Temporomandibular disorders in the medical practice. Journal of family practice. 1996;43:347-57.
- White SC, Pharoah MJ. Oral radiology: principles and interpretation. Amsterdam: Elsevier Health Sciences; 2014.
- Zain-Alabdeen EH, Alsadhan RI. A comparative study of accuracy of detection of surface osseous changes in the temporomandibular joint using multidetector CT and cone beam CT. Dentomaxillofac Radiol. 2012;41:185-91. doi.org/10.1259/dmfr/24985971. PubMed PMID: 22378752. PubMed PMCID: 3520284.
- Tsiklakis K, Syriopoulos K, Stamatakis HC. Radiographic examination of the temporomandibular joint using cone beam computed tomography. Dentomaxillofac Radiol. 2004;33:196-201. doi.org/10.1259/dmfr/27403192. PubMed PMID: 15371321.
- Hussain AM, Packota G, Major PW, Flores-Mir C. Role of different imaging modalities in assessment of temporomandibular joint erosions and osteophytes: a systematic review. Dentomaxillofac Radiol. 2008;37:63-71. doi.org/10.1259/dmfr/16932758. PubMed PMID: 18239033.
- Sindeaux R, Figueiredo PT, de Melo NS, Guimaraes AT, Lazarte L, Pereira FB, et al. Fractal dimension and mandibular cortical width in normal and osteoporotic men and women. Maturitas. 2014;77:142-8. doi.org/10.1016/j.maturitas.2013.10.011. PubMed PMID: 24289895.
- Gaalaas L, Henn L, Gaillard PR, Ahmad M, Islam MS. Analysis of trabecular bone using site-specific fractal values calculated from cone beam CT images. Oral Radiology. 2014;30:179-85. doi.org/10.1007/s11282-013-0163-z.
- Ghodsi M, Hassani H, Sanei S, Hicks Y. The use of noise information for detection of temporomandibular disorder. Biomedical Signal Processing and Control. 2009;4:79-85. doi.org/10.1016/j.bspc.2008.10.001.
- Thevenot J, Chen J, Finnilä M, Nieminen M, Lehenkari P, Saarakkala S, et al. Local binary patterns to evaluate trabecular bone structure from micro-CT data: application to studies of human osteoarthritis. European Conference on Computer Vision: Springer; 2014.
- Mäenpää T, Pietikäinen M. Texture analysis with local binary patterns. Handbook of Pattern Recognition and Computer Vision. 2005;3:197-216. doi.org/10.1142/9789812775320_0011.
- Ojala T, Pietikainen M, Harwood D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. 9-13 Oct. 1994. Jerusalem: Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on; 1994.
- Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern recognition. 1996;29:51-9. doi.org/10.1016/0031-3203(95)00067-4.
- Tajeripour F, Kabir E, Sheikhi A. Fabric defect detection using modified local binary patterns. EURASIP Journal on Advances in Signal Processing. 2008;2008:60. doi.org/10.1155/2008/783898.
- Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence. 2002;24:971-87. doi.org/10.1109/TPAMI.2002.1017623.
- Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004;60:91-110. doi.org/10.1023/B:VISI.0000029664.99615.94.
- Dalal N, Triggs B, editors . Histograms of oriented gradients for human detection. 20-25 June 2005. San Diego: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on; 2005.
- Sadek RA. SVD based image processing applications: state of the art, contributions and research challenges. arXiv preprint arXiv:1211.7102. 2012;3:26–34.
- Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician. 1992;46:175-85.
- Smola AJ, Schölkopf B. A tutorial on support vector regression. Statistics and computing. 2004;14:199-222. doi.org/10.1023/B:STCO.0000035301.49549.88.
- Rish I. An empirical study of the naive Bayes classifier. IJCAI 2001 workshop on empirical methods in artificial intelligence; 2001: IBM New York. New York: IJCAI 2001 workshop on empirical methods in artificial intelligence; 2001.
- Horning N. Introduction to decision trees and random forests. American Museum of Natural History’s. 2013.
- Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. 2011.
- Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition. 1997;30:1145-59. doi.org/10.1016/S0031-3203(96)00142-2.
- Mishra AK, Kim D, Andayana I, editors. Development of three dimensional binary patterns for local bone structure analysis. Bioinformatics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference on; 2011: IEEE.
)