A Method to Recognize Anatomical Site and Image Acquisition View in X-ray Images

Xiao Chang, Thomas Mazur, H. Harold Li, Deshan Yang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A method was developed to recognize anatomical site and image acquisition view automatically in 2D X-ray images that are used in image-guided radiation therapy. The purpose is to enable site and view dependent automation and optimization in the image processing tasks including 2D-2D image registration, 2D image contrast enhancement, and independent treatment site confirmation. The X-ray images for 180 patients of six disease sites (the brain, head-neck, breast, lung, abdomen, and pelvis) were included in this study with 30 patients each site and two images of orthogonal views each patient. A hierarchical multiclass recognition model was developed to recognize general site first and then specific site. Each node of the hierarchical model recognized the images using a feature extraction step based on principal component analysis followed by a binary classification step based on support vector machine. Given two images in known orthogonal views, the site recognition model achieved a 99% average F1 score across the six sites. If the views were unknown in the images, the average F1 score was 97%. If only one image was taken either with or without view information, the average F1 score was 94%. The accuracy of the site-specific view recognition models was 100%.

Original languageEnglish
Pages (from-to)751-760
Number of pages10
JournalJournal of Digital Imaging
Volume30
Issue number6
DOIs
StatePublished - Dec 1 2017

Keywords

  • Classification
  • Image processing
  • Image-guided radiation therapy
  • Machine learning
  • Principal component analysis

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