International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Geetha Virupaiaha* and Aprameya Kittane Sathyanarayanab

aDepartment of Electronics and Communication Engineering, UBDT College of Engineering, Davanagere, Karnataka, India.
bDepartment of Electrical and Electronics Engineering, UBDT College of Engineering, Davanagere, Karnataka, India.

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Different enhancement techniques are used to diagnose dental caries in dental X-ray images. The diagnostic performances of the enhancement methods for caries detection in digital radiographs are evaluated. The proposed method consists of pre-processing of dental X-ray images using Gaussian low pass filter in frequency domain, extraction of statistical features from enhanced image using Support Vector Machine (SVM) classifier. 105 images of normal and dental caries derived using digital radiography are used to evaluate the performance of the SVM classifier with 10-fold cross validation. The images are annotated by a dentist. The quantitative analysis is done after evaluating the performance parameters of SVM classifier. The findings show that the proposed method gives TP rate = 0.982, FP rate = 0.018, ROC area = 0.982, and PRC area = 0.973. By using 2-way ANOVA, the results are tested at significant level of 5%, show that the interaction of enhancement methods with dental images on performance parameter values are significant. The results suggest that the proposed framework is a promising approach for the automatic detection of dental caries in dental radiographs, can also be used for other dental applications. The performance of the system can be further improved by high quality and high quantity dataset and suitable segmentation technique.

Keywords: Digital radiography; computer assisted diagnosis; image enhancement; dental caries; image pre-processing; SVM.

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Received: 2019-01-01
Revised: 2020-02-05
Accepted: 2020-03-10
Publication Date: 2020-03-01

Cite this article:

Virupaiah, G., Sathyanarayana, A.K. 2020. Analysis of image enhancement techniques for dental caries detection using texture analysis and support vector machine. International Journal of Applied Science and Engineering, 17, 75–86.

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