Comparative Analysis of Color Matching System for Teeth Recognition Using Color Moment

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...

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Bibliographic Details
Published in:Medical devices (Auckland, N.Z.) N.Z.), 2019-12, Vol.12, p.497-504
Main Authors: Justiawan, Wahjuningrum, Dian Agustin, Hadi, Ratna Puspita, Nurhayati, Adienda Pajar, Prayogo, Kevin, Sigit, Riyanto, Arief, Zainal
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial neural networks
Cameras (Photography)
Classification
Color
color matching
color moment
Dentistry
Dentists
Digital cameras
Image processing
knn algorithm
Lighting
Original Research
Technology application
Teeth
teeth recognition
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Summary: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. 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. KNN algorithm in RGB characteristic achieves 97.5% within only a 0.02 second computation time. KNN algorithm in RGB characteristic provides the best performance when compared to the other approaches.
ISSN:1179-1470
1179-1470
DOI:10.2147/MDER.S224280