TY - JOUR
T1 - A Method to Recognize Anatomical Site and Image Acquisition View in X-ray Images
AU - Chang, Xiao
AU - Mazur, Thomas
AU - Li, H. Harold
AU - Yang, Deshan
N1 - Funding Information:
Acknowledgements Research reported in this study was supported by the Agency for Healthcare Research and Quality (AHRQ) under award 1R01HS0222888. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency of Healthcare Research and Quality.
Publisher Copyright:
© 2017, Society for Imaging Informatics in Medicine.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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%.
AB - 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%.
KW - Classification
KW - Image processing
KW - Image-guided radiation therapy
KW - Machine learning
KW - Principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=85020501835&partnerID=8YFLogxK
U2 - 10.1007/s10278-017-9981-6
DO - 10.1007/s10278-017-9981-6
M3 - Article
C2 - 28623558
AN - SCOPUS:85020501835
SN - 0897-1889
VL - 30
SP - 751
EP - 760
JO - Journal of Digital Imaging
JF - Journal of Digital Imaging
IS - 6
ER -