Generation of Teeth Caries Features for Human Dental Caries Classification

Linda Wahyu Widianti, Sarifuddin Madenda, Johan Harlan, Sunny Sudiro, Farina Pramanik

Abstract


Many dental diseases are experienced by humans, one of which is dental caries, there are three types of human dental caries, namely enamel caries, dentin caries and pulp caries. This study contains the detection of caries disease in human teeth using two-dimensional images and radiological results of x-ray periapical radiographs from a test image dataset that has a number of pixels between 374x288 to 672x514 pixels with an image resolution of 96 DPI. The original data of existing dental images was processed using Matlab language to obtain caries features through three stages of the processes: pre-processing stage which are stages of the preprocessing process that converts data from a two-dimensional color image (row/height, column/width) that is stored using three channels Red, Green and Blue (RGB), into a grayscale image with one channel, the process of extracting dental caries features by performing calculations caries area and calculate the distance of the caries area to the nerve canal (pulp), and the process of building learning or reference data from dental caries using 24 radiograph periapical data on molar tooth images processed using Matlab. Dental caries features extraction process and the features learning process to generate references features from dental caries is the main objective of this research. This study result was references features for human dental caries classification.

Keywords


dental image;detection;features;learning data;dental caries;periapical radiograph

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References


Bhagyashree V. Shivpuje , Dr. G. S. Sable, ‘A Review on Digital Dental Radiographic Images for Disease Identification and Classification’, Int. Journal of Engineering Research and Application, 2016

Madenda S, ‘Pengolahan Citra Digital & Video Digital, Teori Aplikasi, dan Pemrograman Menggunakan Matlab’, Erlangga, Jakarta, 2015

Bernard Y. Tumbelaka, Fahmi Oscandar, Faisal Nur Baihaki, Suhardjo Sitam, Mandojo Rukmo,’ Identify pulpitis at dental X-ray periapical radiography based on edge detection, texture description and artificial neural networks’, Conference: The 19th International Congress of Dento-Maxillo-Facial Radiology, 2013

Eric Whites, Nicholas drage, ‘Essensials of Dental Radiography and Radiology’, Elsevier, Fifth edition UK, 2013

C. White S. Oral Radiology (Principle and Interpretation). Vol. 53, Journal of Chemical Information and Modeling, pp1689-1699, 2013

Khushbu Yadav, Satyam Prakash,’Dental Caries: A Review’, Asian Journal of Biomedical and Pharmaceutical Sciences, 2016

Richkne C. Scheid, Gabriel Weiss, ‘Woelfel’s Dental Anatomy’, Wolters Kluwers Health Inc, eight edition, 2014.

D. P. Tian and B. Shaanxi, “A Review on Image Feature Extraction and Representation”, Techniques International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385-396, 2013

D. Lu and Q. WENG, “A survey of image classification methods and techniques for improving classification performance”, International Journal of Remote Sensing, Vol. 28, No. 5, pp. 823–870, 2007

M Manoj krishna, M Neelima , M Harshali , M Venu Gopala Rao, “Image classification using Deep learning”, International Journal of Engineering & Technology, Vol 7, issue (2.7), pp. 614-617, 2018.

Medjahed Seyyid Ahmed, “A Comparative Study of Feature Extraction Methods in Images Classification”, Image, Graphics and Signal Processing, vol 3, pp 16-23, 2015

Mimansha Pate, Nitin Pate, “Exploring Research Methodology: Review Article”, International Journal of Research & Review, Vol.6, Issue: 3, pp. 48 – 55, 2019

Beniwal Sunita and Arora Jitender,’ Classification and Feature Selection Techniques in Data Mining’, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, vol. 1 Issue 6, August, 2012

Kumar Sandeep, Balyan Aman, Chawla Manvi,’ Object Detection and Recognition in Images’, International Journal of Engineering Development and Research, Volume 5, Issue 4 | ISSN: 2321-9939, 2017

Abu Mohd azlan and Indra nuruh azirah, ‘A study on Image Classification based on Deep Learnin and Tensorflow’, International Journal of Engineering Research and Technology. ISSN 0974-3154, vol 12, no. 4, pp. 563-569, 2019




DOI: http://dx.doi.org/10.22441/incomtech.v11i3.13804

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pISSN: 2085-4811
eISSN: 2579-6089
Jurnal URL: http://publikasi.mercubuana.ac.id/index.php/Incomtech
Jurnal DOI: 10.22441/incomtech

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