Automatic identification of chest orientation in digital radiographic images

Persistent Link:
http://hdl.handle.net/10150/282811
Title:
Automatic identification of chest orientation in digital radiographic images
Author:
Evanoff, Michael Geoffrey, 1964-
Issue Date:
1998
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Radiology departments are implementing conversion from the use of hard copy film in favor of digital imaging. New digital acquisitions are increasing the efficacy of radiological imaging. The outputs of new modalities such as magnetic resonance (MR) and computed tomography (CT) are digital. They both involve gathering information that allows reconstructing cross sectional projections of internal structures and displaying them as digital images. Other technologies, e.g., computed radiography (CR), can provide digital radiographic data that replaces analog projection radiography. To date, the processed digital data is still transferred to film to provide a typical radiographic film in appearance. The film is presented to the doctor for diagnostic review. The research in this dissertation is concerned with making a film-less department. It specifically addresses problems in presenting CR images to the physician. The goal of this research is to create a computer recognition algorithm that will automatically recognize the orientation and discriminate between the lateral and posteroanterior view of digital chest radiographs image. The algorithm maintains 91.9% accuracy rate. The recognition takes .15 second per image.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Biomedical.; Engineering, Electronics and Electrical.; Health Sciences, Radiology.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Tharp, Hal

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAutomatic identification of chest orientation in digital radiographic imagesen_US
dc.creatorEvanoff, Michael Geoffrey, 1964-en_US
dc.contributor.authorEvanoff, Michael Geoffrey, 1964-en_US
dc.date.issued1998en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractRadiology departments are implementing conversion from the use of hard copy film in favor of digital imaging. New digital acquisitions are increasing the efficacy of radiological imaging. The outputs of new modalities such as magnetic resonance (MR) and computed tomography (CT) are digital. They both involve gathering information that allows reconstructing cross sectional projections of internal structures and displaying them as digital images. Other technologies, e.g., computed radiography (CR), can provide digital radiographic data that replaces analog projection radiography. To date, the processed digital data is still transferred to film to provide a typical radiographic film in appearance. The film is presented to the doctor for diagnostic review. The research in this dissertation is concerned with making a film-less department. It specifically addresses problems in presenting CR images to the physician. The goal of this research is to create a computer recognition algorithm that will automatically recognize the orientation and discriminate between the lateral and posteroanterior view of digital chest radiographs image. The algorithm maintains 91.9% accuracy rate. The recognition takes .15 second per image.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Biomedical.en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectHealth Sciences, Radiology.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorTharp, Halen_US
dc.identifier.proquest9912115en_US
dc.identifier.bibrecord.b39123297en_US
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