The performance of contrast enhancement based on sharp filter for digital intra-oral dental radiograph images

Authors:
Siti Arpah Ahmad;Mohd Nasir Taib;Noor Elaiza Abd Khalid;Haslina Taib;Norazan Mohamed Ramli
Affiliations:
Faculty of Computer and Mathematical Sciences, Faculty of Electrical and Electronic Engineering, Universiti Teknologi MARA Malaysia, Shah Alam, Selangor, Malaysia;Faculty of Computer and Mathematical Sciences, Faculty of Electrical and Electronic Engineering, Universiti Teknologi MARA Malaysia, Shah Alam, Selangor, Malaysia;Faculty of Computer and Mathematical Sciences, Faculty of Electrical and Electronic Engineering, Universiti Teknologi MARA Malaysia, Shah Alam, Selangor, Malaysia;Faculty of Computer and Mathematical Sciences, Faculty of Electrical and Electronic Engineering, Universiti Teknologi MARA Malaysia, Shah Alam, Selangor, Malaysia;Faculty of Computer and Mathematical Sciences, Faculty of Electrical and Electronic Engineering, Universiti Teknologi MARA Malaysia, Shah Alam, Selangor, Malaysia
Venue:
Proceedings of the 15th WSEAS international conference on Computers
Year:
2011

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Abstract

This paper presents an analysis on the contrast enhancement function combined with sharpening filter applied on digital Intra-oral dental radiographs images. Intra-oral dental radiograph is a common medium used in dentistry to examine gum related diseases. Due to the fact that radiograph images usually are noisy and low in contrast, research related to image enhancement is an active research area. This work compares the original digital intra-oral dental radiograph and images enhanced with sharpening filter as a main filter for dentists' evaluation. Two types of compound enhancement algorithm with the sharpening filter named Sharp Median Adaptive Histogram Equalization (SMAHE) and Sharp Contrast Limited Adaptive Histogram Equalization (SCLAHE) are compared with the original images. Results show that SCLAHE outperforms original and SMAHE in enhancing certain pathologies. However, based on the original images SMAHE shows an improvement in detecting faulty abnormality and performs better than SCLAHE.