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Justiawan .
Politeknik Elektronika Negeri Surabaya
Computer Science & IT

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Tooth Color Detection Using PCA and KNN Classifier Algorithm Based on Color Moment Justiawan .; Riyanto Sigit; Zainal Arief
EMITTER International Journal of Engineering Technology Vol 5 No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1114.623 KB) | DOI: 10.24003/emitter.v5i1.171

Abstract

Matching the suitable color for tooth reconstruction is an important step that can make difficulties for the dentists due to the subjective factors  of color selection. Accurate color matching system is mainly result based on images analyzing and processing techniques of recognition system.  This system consist of three parts, which are data collection from digital teeth color images, data preparation for taking color analysis technique and extracting the features, and data classification involve feature selection for reducing the features number of this system. The teeth images which is used in this research are 16 types of teeth that are taken from RSGM UNAIR SURABAYA. Feature extraction is taken by the characteristics of the RGB, HSV and LAB based on the color moment calculation such as mean, standard deviation, skewness, and kurtosis parameter. Due to many formed features from each color space, it is required addition method for reducing the number of features by choosing the essential information like Principal Component Analysis (PCA) method. Combining the PCA feature selection technique to the clasification process using K Nearest Neighbour (KNN) classifier  algorithm can be improved the accuracy performance of this system. On the experiment result, it showed that only using  KNN classifier achieve accuracy percentage up to 97.5 % in learning process and 92.5 % in testing process while combining PCA with KNN classifier can reduce the 36 features to the 26 features which can improve the accuracy percentage up to 98.54 % in learning process and  93.12% in testing process. Adding PCA as the feature selection method can be improved the accuracy performance of this color matching system with little number of features. 
Co-Authors Riyanto Sigit Zainal Arief