Abstract
Landmarks needed for detecting dental abnormalities in cephalometric analysis were selected from the digital image, and the angle values needed for dental analysis were calculated and stored in a database which is used for developing training dataset. Principal component analysis was applied for dimension reduction to get the desired feature vectors which are trained and tested using support vector machine and proximal support vector machine classifier to detect the dental abnormalities, the performance of the classifiers were also compared.
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References
Banumathi A, Raju S, Abhaikumar V (2011) Diagnosis of dental deformities in cephalometry images using support vector machine. J Med Syst 35:113–119
Baumrind S, Miller DM (1980) Computer- aided head film analysis: the University of San Francisco method. Am J Orthod 78:41–65
Cortes C, Vapnik V (1995) Support vector network. Mach Learn 20(2):273–297
Fodor IK (2002) A survey of dimension reduction techniques. Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, P.O. Box 808, L-560, Livermor, fodor1@llnl.gov
Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. Department of computer science, University Of Wisconsin, Madison
Hotelling H (1933) Analysis of a complex of statistical variables into principle components. Phil Mag 24:417–441
Hsu C, Chang C, Lin C (2010) A practical guide to support vector classification. National Taiwan University, Taiwan
Jain A, Mondal T, Sardana HK (2010) A novel strategy for automatic localization of cephalometric landmarks. IEEE Int Conf Comput Eng Technol (ICCET) 3:v3-284–v3-288
Jetwani1 DP, Kumar S, Sardana HK (2011) Cephalometric landmark identification using fuzzy wavelet edge detector. In: IEEE international workshop on medical measurement and application (MeMeA), pp 349–353
Martina R, Teti R, Addona DD, Iodice G (2004) Neural network based system for decision making support in orthodontic extractions. I*PROMS
Meyer D (2011) Support vector machines. The interface to libsvm in package e1071, Tchnische University
Mondal T, Jain A, Sardana HK (2011) Automatic craniofacial structure detection on cephalometric images. IEEE Trans Image Process 20:2606–2614
Mosleh MAA, Baba MS, Himazian N, AL-Makramani BMA (2008) An image processing system for cephalometric analysis and measurement. IEEE Int Symp Inf Technol 4:1–8
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 559–572
Rakosi T (1982) Cephalometric radiography. Wolfe medical publication limited, London
Romaniuk B, Desvignes M, Revenu M, Deshayes MJ (2002) Linear and non-linear model for statistical localization of landmark. Proceedings, 16th international conference pattern recognition, vol 4, pp 393–396
Weng JJ (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18:831–836
Zhuang D, Zhang B, Yang Q, Yan J, Chen Z, Chen Y (2005) Efficient text classification by weighted proximal SVM. Fifth IEEE international conference on data mining (ICDM), pp-8
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Arulselvi, M., Ramalingam, V. & Palanivel, S. Detection of dental abnormalities using SVM and PSVM. AI & Soc 29, 69–74 (2014). https://doi.org/10.1007/s00146-013-0440-8
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DOI: https://doi.org/10.1007/s00146-013-0440-8