Aprameya K. S

Work place: Department of Electrical and Electronics Engineering, University BDT College of Engineering, Davanagere – 577004, Karnataka, India.



Research Interests: Image Processing


Dr. K.S. Aprameya obtained his BE degree in Electrical and Electronics Engineering in 1989, M.Tech. degree in Digital Electronics and Communication Systems in 1993 from University of Mysore and awarded Ph.D. degree in 2009 from Indian Institute of Technology, Roorkee. He is a life member for ISTE, New Delhi, MIE for “The Institution of Engineers (India)” and a member of Ultrasonic Society of India, National Physical Laboratory, New Delhi. He worked as Honorary Secretary for The Institution of Engineers (India) Davanagere Local Centre, Karnataka, India (2012-14). He is interested in image processing and its applications and ultrasonic instrumentation systems.

Author Articles
Textural Analysis Based Classification of Digital X-ray Images for Dental Caries Diagnosis

By Geetha V Aprameya K. S

DOI: https://doi.org/10.5815/ijem.2019.03.04, Pub. Date: 8 May 2019

In this paper, we propose a suitable textural feature for diagnosis of dental caries in digital radiographs. The dental diagnosis system consists of Laplacian filter for image sharpening, adaptive threshold and morphological operations for segmentation, and support vector machine (SVM) as a classifier. In segmented image, textural features are extracted, and applied to the classifier, to classify the image as caries or normal. Experimental results indicate that GLCM (Grey Level Co-occurrence Matrix) and GLDM (Grey Level Difference Method) textural features are giving better performance measures as compared to other types of textural features with an accuracy of 96.88%, sensitivity of 1, specificity of 0.8667 and precision of 96.08%. The data were analyzed by Analysis of Variance (ANOVA), at a significant level of 5%. This result indicates that the interaction of feature extraction methods on performance measures are significant. Hence, GLCM or GLDM features provide reliable decision support for dental caries diagnosis.

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