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http://dx.doi.org/10.5370/KIEE.2010.59.6.1136

Individual Tooth Image Segmentation with Correcting of Specular Reflections  

Lee, Seong-Taek (건국대 의학공학부)
Kim, Kyeong-Seop (건국대 의료생명대 의학공학부, 건국대 의공학실용기술연구소)
Yoon, Tae-Ho (건국대 의학공학부)
Lee, Jeong-Whan (건국대 의료생명대 의학공학부)
Kim, Kee-Deog (연세대 치과대학병원 통합진료과)
Park, Won-Se (연세대 치과대학병원 통합진료과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.59, no.6, 2010 , pp. 1136-1142 More about this Journal
Abstract
In this study, an efficient removal algorithm for specular reflections in a tooth color image is proposed to minimize the artefact interrupting color image segmentation. The pixel values of RGB color channels are initially reversed to emphasize the features in reflective regions, and then those regions are automatically detected by utilizing perceptron artificial neural network model and those prominent intensities are corrected by applying a smoothing spatial filter. After correcting specular reflection regions, multiple seeds in the tooth candidates are selected to find the regional minima and MCWA(Marker-Controlled Watershed Algorithm) is applied to delineate the individual tooth region in a CCD tooth color image. Therefore, the accuracy in segmentation for separating tooth regions can be drastically improved with removing specular reflections due to the illumination effect.
Keywords
Tooth; Color Image; Perceptron; Image Segmentation; Artificial Neural Network (ANN);
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