Arna Fariza, - and Agus Zainal Arifin, - and Eha Renwi Astuti, - (2020) Automatic Tooth and Background Segmentation in Dental X-ray Using U-Net Convolution Network. IEEE. ISSN 00189219, 15582256

	Text (ARTIKEL) 45. Automatic Tooth and Background Segmentation in Dental X-ray Using U-Net Convolution Network.pdf Download (948kB)
	Text (TURNITIN) 45. 21% Automatic Tooth and Background Segmentation in Dental X-ray Using U-Net Convolution Network.pdf Download (1MB)
	Text (VALIDASI) 45. Automatic Tooth and Background Segmentation in Dental X-ray Using U-Net Convolution Network.pdf Download (209kB)
	Text (KARIL) 39. Automatic Tooth and Background Segmentation in Dental X-ray Using U-Net Convolution.pdf Download (114kB)

Abstract

Tooth and background segmentation in dental Xray is used to produce an area of a tooth by removing areas of tissue and other neighboring teeth. This presents challenges due to the large number of superimposed (overlapping) images of teeth between the adjacent and adjacent teeth and the difficulty of determining the area of the tooth with other tissues. This study proposes a new approach for automatic segmentation of dental X-ray images using the U-Net convolution network. The stages used in the training process consist of data augmentation, pre-processing with Contrast Limited Adequate Histogram Equalization (CLAHE) and gamma adjustment, and training with the U-Net architecture. While the testing process consists of pre-processing, prediction, and removing small areas in the background. The average accuracy of the U-Net convolutional network segmentation accuracy achieved excellent results, 97.60%.

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