Comparative Analysis of Color Matching System for Teeth Recognition Using Color Moment
Received 17 September 2019
Accepted for publication 22 November 2019
Published 30 December 2019 Volume 2019:12 Pages 497—504
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Scott Fraser
Justiawan,1 Dian Agustin Wahjuningrum,2 Ratna Puspita Hadi,2 Adienda Pajar Nurhayati,2 Kevin Prayogo,2 Riyanto Sigit,3 Zainal Arief4
1Department of Research and Development, Tione Indonesia Jaya, Surabaya 60111, Indonesia; 2Department of Conservative Dentistry, Faculty of Dental Medicine, Universitas Airlangga, Surabaya 60131, Indonesia; 3Department of Informatic Engineering, Magister Program of Engineering Technology, Electronic Engineering Polytechnic Institute of Surabaya, Surabaya, Indonesia; 4Department of Electrical Engineering, Magister Program of Engineering Technology, Electronic Engineering Polytechnic Institute of Surabaya, Surabaya, Indonesia
Correspondence: Dian Agustin Wahjuningrum
Department of Conservative Dentistry, Faculty of Dental Medicine, Universitas Airlangga, Mayjen Prof. Dr. Moestopo 47, Surabaya, East Java 60131, Indonesia
Tel +62 315030252
Fax +62 315022472
Background: In recent years, veritable image processing systems have been developed for several field applications, some of which are recognition and classification. One such application is in the medical field for teeth color matching systems. The color matching technique is a feasible solution for classifying patients’ teeth images to evaluate the suitable treatment of tooth replacement in dentistry. However the lighting conditions of the environment and visual teeth color deficiency will be influenced or affected by the color matching performance.
Methods: This paper proposes the comparative analysis of a color matching system, using K-nearest neighbors (KNN), neural network (NN), and decision tree (DT) algorithms to classify and recognize 16 types of dental images of persons that used several extracted features, from shade guide of teeth, with a digital camera, ranging from 250–300 lux lighting value. The extracted features are produced from RGB, HSV, and Lab color moment characteristic calculation of tooth samples. Those features were compared with input images using euclidean distance value.
Results: KNN algorithm in RGB characteristic achieves 97.5% within only a 0.02 second computation time.
Conclusion: KNN algorithm in RGB characteristic provides the best performance when compared to the other approaches.
Keywords: KNN algorithm, teeth recognition, color matching, color moment
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