Analysis of Image Segmentation Techniques for Diagnosis of Dental Caries in X-ray Images
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Analysis of Image Segmentation Techniques for Diagnosis of Dental Caries in X-ray Images

Authors: V. Geetha, K. S. Aprameya


Early diagnosis of dental caries is essential for maintaining dental health. In this paper, method for diagnosis of dental caries is proposed using Laplacian filter, adaptive thresholding, texture analysis and Support Vector Machine (SVM) classifier. Analysis of the proposed method is compared with Otsu thresholding, watershed segmentation and active contouring method. Adaptive thresholding has comparatively better performance with 96.9% accuracy and 96.1% precision. The results are validated using statistical method, two-way ANOVA, at significant level of 5%, that shows the interaction of proposed method on performance parameter measures are significant. Hence the proposed technique could be used for detection of dental caries in automated computer assisted diagnosis system.

Keywords: Computer assisted diagnosis, dental caries, dental radiography, image segmentation.

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[1] Pierre Gravel, Gilles Beaudoin, and Jacques A De Guise, “A method for modelling noise in medical images,” IEEE Trans. On Medical imaging, Vol. 23, No. 10, October 2004.
[2] P. Suetens, Fundamentals of Medical Imaging, Cambridge University Press, Second Edition, New York, 2009.
[3] Anil K. Jain and, Hong Chen, “Matching of dental X-ray images for human identification,” Pattern Recognition, vol. 37, pp. 1519 – 1532, 2004.
[4] T. Kondo, S. Ong, and K. Foong, “Tooth segmentation of dental study models using range images,”, IEEE Trans. Med. Imaging, vol. 23, no. 3, pp. 350-362, 2004.
[5] Pedro H. M. Lira, Gilson A. Giraldi, and Luiz A. P. Nevesy, “Using the Mathematical Morphology and Shape Matching for Automatic Data Extraction in Dental X-Ray Images”.
[6] J. Zhou, and M. Abdel, “A content-based system for human identification based on bitewing dental X-ray images,” Pattern Recognition, vol. 38, no. 11, pp. 2132-2142, 2005.
[7] O. Nomir, and M. Abdel, “A system for human identification for human identification from X-ray dental radiographs,” Pattern Recognition, vol. 38, no. 11, pp. 1295-1305, 2005.
[8] S. Li, T. Fevens, and A. Krzyzak, “An automatic variational level set segmentation framework for computer aided dental X-ray analysis in clinical environments,” Comput Med Imaging Graph, vol. 30, pp. 65-74, 2006.
[9] P.L.Lin, Y.H.Lai, and P.W.Huang, “An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information,” Pattern Recognition, vol. 43, pp. 1380–1392, 2010.
[10] P.L. Lin, P.W. Huang, Y.S. Cho, and C.H. Kuo, “An Automatic and Effective Tooth Isolation Method for Dental Radiographs,” Opto−Electronics Review, vol. 21, pp. 126–136, 2013.
[11] A.Farzana Shahar Banu, M. Kayalvizhi, Dr. Banumathi Arumugam, and Dr. Ulaganathan Gurunathan, “Texture Based Classification of Dental Cysts,” IEEE International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014.