Automatic Tooth and Background Segmentation in Dental X-ray Using U-Net Convolution Network

Arna Fariza, Agus Zainal Arifin, Eha Renwi Astuti

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2020 6th International Conference on Science in Information Technology
Subtitle of host publicationEmbracing Industry 4.0: Towards Innovation in Disaster Management, ICSITech 2020
EditorsAnita Ahmad Kasim, Andri Pranolo, Leonel Hernandez, Aji Prasetya Wibawa, Roman Voliansky, Hajra Rasmita Ngemba, Rafal Drezewski, Zachir Zachir, Haviluddin Haviluddin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages144-149
Number of pages6
ISBN (Electronic)9781728173498
DOIs
Publication statusPublished - 21 Oct 2020
Event6th International Conference on Science in Information Technology, ICSITech 2020 - Palu, Indonesia
Duration: 21 Oct 202022 Oct 2020

Publication series

Name2020 6th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Disaster Management, ICSITech 2020

Conference

Conference6th International Conference on Science in Information Technology, ICSITech 2020
Country/TerritoryIndonesia
CityPalu
Period21/10/2022/10/20

Keywords

  • U-Net convolution network
  • dental X-ray
  • tooth and background segmentation

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