research-article

Line Operator as Preprocessing Method for CNN-based Osteoporosis Detection in Dental Panoramic Radiograph

Authors:

Ilham Gurat Adillion,

Yoshiteru Ishida,

Agus Zainal ArifinAuthors Info & Claims

ICFET '20: Proceedings of the 6th International Conference on Frontiers of Educational Technologies

Pages 103 - 107

https://doi.org/10.1145/3404709.3404767

Published: 13 July 2020 Publication History

Abstract

Osteoporosis is a disease that can be detected via the trabecular bone pattern in Dental Panoramic Radiograph (DPR). Trabecular bone pattern is difficult to see by the naked eye due to the low contrast and low resolution of DPR. This can affect the performance of osteoporosis disease detection using Convolutional Neural Network (CNN). In this paper we propose the use of Line Operator (LO) on DPR images as a preprocessing method to enhance trabecular bone pattern for CNN-based osteoporosis detection. LO is a method that can enhance line-like structures in medical images such as retina and DPR dataset. To study the effect of LO on CNN-based osteoporosis detection, the performance of non-preprocessed images, LO-preprocessed images and LO + histogram equalization pre-processed images was compared. Results showed that LO-preprocessed images give best osteoporosis detection accuracy of 0.875

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Cited By

Sathish Kumar LPrabu APandimurugan VRajasoundaran SMalla PRoutray S(2022)A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray imagesConcurrency and Computation: Practice and Experience10.1002/cpe.730734:26Online publication date: 5-Sep-2022
https://doi.org/10.1002/cpe.7307

Index Terms

Line Operator as Preprocessing Method for CNN-based Osteoporosis Detection in Dental Panoramic Radiograph
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

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cover image ACM Other conferences

ICFET '20: Proceedings of the 6th International Conference on Frontiers of Educational Technologies

June 2020

235 pages

ISBN:9781450375337

DOI:10.1145/3404709

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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New York, NY, United States

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Published: 13 July 2020

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ICFET 2020: 2020 The 6th International Conference on Frontiers of Educational Technologies

June 5 - 8, 2020

Tokyo, Japan

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Cited By

Sathish Kumar LPrabu APandimurugan VRajasoundaran SMalla PRoutray S(2022)A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray imagesConcurrency and Computation: Practice and Experience10.1002/cpe.730734:26Online publication date: 5-Sep-2022
https://doi.org/10.1002/cpe.7307

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