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 - 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 -