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Bone suppression for chest X-ray image using a convolutional neural filter

  • Naoki Matsubara
  • Atsushi TeramotoEmail author
  • Kuniaki Saito
  • Hiroshi Fujita
Scientific Paper
  • 27 Downloads

Abstract

Chest X-rays are used for mass screening for the early detection of lung cancer. However, lung nodules are often overlooked because of bones overlapping the lung fields. Bone suppression techniques based on artificial intelligence have been developed to solve this problem. However, bone suppression accuracy needs improvement. In this study, we propose a convolutional neural filter (CNF) for bone suppression based on a convolutional neural network which is frequently used in the medical field and has excellent performance in image processing. CNF outputs a value for the bone component of the target pixel by inputting pixel values in the neighborhood of the target pixel. By processing all positions in the input image, a bone-extracted image is generated. Finally, bone-suppressed image is obtained by subtracting the bone-extracted image from the original chest X-ray image. Bone suppression was most accurate when using CNF with six convolutional layers, yielding bone suppression of 89.2%. In addition, abnormalities, if present, were effectively imaged by suppressing only bone components and maintaining soft-tissue. These results suggest that the chances of missing abnormalities may be reduced by using the proposed method. The proposed method is useful for bone suppression in chest X-ray images.

Keywords

Chest X-ray Bone suppression Lung Nodule Convolutional neural network Image processing 

Notes

Acknowledgements

This research was partially supported by a Grant-in-Aid for Scientific Research on Innovative Areas (Multidisciplinary Computational Anatomy, No. 26108005) and a Grant-in-Aid for Scientific Research (No. 17K09070), MEXT, Japan.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain patient data.

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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  1. 1.Graduate School of Health SciencesFujita Health UniversityToyoake-cityJapan
  2. 2.Department of Electrical, Electronic & Computer Engineering, Faculty of EngineeringGifu UniversityGifu-cityJapan

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