Vertebrae segmentation from X-ray images using convolutional neural network

Min Jun Fu, Chii Jen Lin, Yung Nien Sun, Chan Pang Kuok, Ming Huwi Horng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The X-ray examination can effectively help for the diagnosis and analysis of spinal diseases because it possesses the properties of fast, non-invasive, low radiation dose and low cost. In order to obtain the valuable quantitative information of the spine, the automated computer aided tools are developed. The segmentation of vertebrae is an important step to analyze the disease severity from the spinal X-ray images. Although there were numerous studies for automatic vertebrae segmentation in the literature, the segmentation of vertebrae from the frontal X-ray images is still a challenging topic. In this paper, a hybrid method is proposed to segment the thoracic and lumbar vertebrae from an anteroposterior (AP) full spine X-ray image. We apply image processing techniques to detect the vertebral regions and then use the convolutional neural network (CNN) to segment the vertebrae. The segmentation performance using the proposed method is remarkably high with DSC value of 0.941.

Original languageEnglish
Title of host publicationIHIP 2018 - 2018 International Conference on Information Hiding and Image Processing
PublisherAssociation for Computing Machinery
Pages57-61
Number of pages5
ISBN (Electronic)9781450365468
DOIs
Publication statusPublished - 2018 Sep 22
Event1st International Conference on Information Hiding and Image Processing, IHIP 2018 - Manchester, United Kingdom
Duration: 2018 Sep 222018 Sep 24

Publication series

NameACM International Conference Proceeding Series

Conference

Conference1st International Conference on Information Hiding and Image Processing, IHIP 2018
CountryUnited Kingdom
CityManchester
Period18-09-2218-09-24

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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