TY - GEN
T1 - Automatic Tooth and Background Segmentation in Dental X-ray Using U-Net Convolution Network
AU - Fariza, Arna
AU - Arifin, Agus Zainal
AU - Astuti, Eha Renwi
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/21
Y1 - 2020/10/21
N2 - Tooth and background segmentation in dental X-ray is used to produce an area of a tooth by removing areas of tissue and other neighboring teeth. This presents challenges due to a large number of superimposed (overlapping) images of teeth between the adjacent teeth and the difficulty of determining the area of the tooth with other tissues automatically. This study proposes a new approach for the 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 experimental results show the average accuracy of the proposed U-Net convolutional network segmentation accuracy achieves excellent results, 97.61% compared to spatial Fuzzy C-means with gaussian kernel-based of 65.55%. It shows the proposed method achieves superior automatic tooth and background segmentation. The experiment result among 1907 image testing, there are 14.58% producing segmentation because of biased boundaries on the tissue at the root of the tooth and overlapping images on the enamel.
AB - Tooth and background segmentation in dental X-ray is used to produce an area of a tooth by removing areas of tissue and other neighboring teeth. This presents challenges due to a large number of superimposed (overlapping) images of teeth between the adjacent teeth and the difficulty of determining the area of the tooth with other tissues automatically. This study proposes a new approach for the 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 experimental results show the average accuracy of the proposed U-Net convolutional network segmentation accuracy achieves excellent results, 97.61% compared to spatial Fuzzy C-means with gaussian kernel-based of 65.55%. It shows the proposed method achieves superior automatic tooth and background segmentation. The experiment result among 1907 image testing, there are 14.58% producing segmentation because of biased boundaries on the tissue at the root of the tooth and overlapping images on the enamel.
KW - U-Net convolution network
KW - dental X-ray
KW - tooth and background segmentation
UR - http://www.scopus.com/inward/record.url?scp=85104520670&partnerID=8YFLogxK
U2 - 10.1109/ICSITech49800.2020.9392039
DO - 10.1109/ICSITech49800.2020.9392039
M3 - Conference contribution
AN - SCOPUS:85104520670
T3 - 2020 6th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Disaster Management, ICSITech 2020
SP - 144
EP - 149
BT - 2020 6th International Conference on Science in Information Technology
A2 - Kasim, Anita Ahmad
A2 - Pranolo, Andri
A2 - Hernandez, Leonel
A2 - Wibawa, Aji Prasetya
A2 - Voliansky, Roman
A2 - Ngemba, Hajra Rasmita
A2 - Drezewski, Rafal
A2 - Zachir, Zachir
A2 - Haviluddin, Haviluddin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Science in Information Technology, ICSITech 2020
Y2 - 21 October 2020 through 22 October 2020
ER -