Dental Image Segmentation based on Multiple Linear Regression Correlates with Normalized RGB Values

Li Feng, Lequan Min, Min Chen, Wenyuan Kan, and Qingxian Luan


Dental tooth image, normalized RGB value, segmentation, erosion technique, cellular neural network, dilation


This paper presented an approach for segmenting dental tooth images based on multiple linear regression correlates with normalized RGB values, which provides preparatory work to form a complete progress for dental plaque quantification. The approach was applied to clinical dental tooth images. The experimental results showed that this method provided objective, and time-saving segmentations compared with those of traditional multiple linear regression correlates with RGB values. Dilation and erosion techniques are used to increase the efficacy of the dental tooth image processing.

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