A Method to Recognize Anatomical Site and Image Acquisition View in X-ray Images.
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 registrati...
| Publicado en: | Journal of Digital Imaging Vol. 30; no. 6; pp. 751 - 761 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126169907&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126169907 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2017 vid: 30 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 126169907 126169907 144029722 126169907 10.1007/s10278-017-9981-6 126169907 ppf: 751 ppct: 10 formats: fmt: @attributes: type: P tig: atl: A Method to Recognize Anatomical Site and Image Acquisition View in X-ray Images. aug: au: Chang, Xiao Mazur, Thomas Li, H. Yang, Deshan affil: Department of Radiation Oncology , Washington University School of Medicine , St. Louis USA sug: subj: Radiotherapy Evaluation Image Processing, Computer Assisted Classification Automation, Laboratory Radiographic Image Interpretation, Computer-Assisted Methods Human X-Rays Patients Body Regions Image Enhancement Factor Analysis Machine Learning 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%. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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