Development and image quality assessment of a contrast-enhancement algorithm for display of digital chest radiographs.

Persistent Link:
http://hdl.handle.net/10150/185844
Title:
Development and image quality assessment of a contrast-enhancement algorithm for display of digital chest radiographs.
Author:
Rehm, Kelly.
Issue Date:
1992
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:
This dissertation presents a contrast-enhancement algorithm called Artifact-Suppressed Adaptive Histogram Equalization (ASAHE). This algorithm was developed as part of a larger effort to replace the film radiographs currently used in radiology departments with digital images. Among the expected benefits of digital radiology are improved image management and greater diagnostic accuracy. Film radiographs record X-ray transmission data at high spatial resolution, and a wide dynamic range of signal. Current digital radiography systems record an image at reduced spatial resolution and with coarse sampling of the available dynamic range. These reductions have a negative impact on diagnostic accuracy. The contrast-enhancement algorithm presented in this dissertation is designed to boost diagnostic accuracy of radiologists using digital images. The ASAHE algorithm is an extension of an earlier technique called Adaptive Histogram Equalization (AHE). The AHE algorithm is unsuitable for chest radiographs because it over-enhances noise, and introduces boundary artifacts. The modifications incorporated in ASAHE suppress the artifacts and allow processing of chest radiographs. This dissertation describes the psychophysical methods used to evaluate the effects of processing algorithms on human observer performance. An experiment conducted with anthropomorphic phantoms and simulated nodules showed the ASAHE algorithm to be superior for human detection of nodules when compared to a computed radiography system's algorithm that is in current use. An experiment conducted using clinical images demonstrating pneumothoraces (partial lung collapse) indicated no difference in human observer accuracy when ASAHE images were compared to computed radiography images, but greater ease of diagnosis when ASAHE images were used. These results provide evidence to suggest that Artifact-Suppressed Adaptive Histogram Equalization can be effective in increasing diagnostic accuracy and efficiency.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Dissertations, Academic.; Radiography -- Image quality.; Radiography, Medical -- Digital techniques.; Radiology.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Optical Sciences; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Dallas, William J.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleDevelopment and image quality assessment of a contrast-enhancement algorithm for display of digital chest radiographs.en_US
dc.creatorRehm, Kelly.en_US
dc.contributor.authorRehm, Kelly.en_US
dc.date.issued1992en_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.abstractThis dissertation presents a contrast-enhancement algorithm called Artifact-Suppressed Adaptive Histogram Equalization (ASAHE). This algorithm was developed as part of a larger effort to replace the film radiographs currently used in radiology departments with digital images. Among the expected benefits of digital radiology are improved image management and greater diagnostic accuracy. Film radiographs record X-ray transmission data at high spatial resolution, and a wide dynamic range of signal. Current digital radiography systems record an image at reduced spatial resolution and with coarse sampling of the available dynamic range. These reductions have a negative impact on diagnostic accuracy. The contrast-enhancement algorithm presented in this dissertation is designed to boost diagnostic accuracy of radiologists using digital images. The ASAHE algorithm is an extension of an earlier technique called Adaptive Histogram Equalization (AHE). The AHE algorithm is unsuitable for chest radiographs because it over-enhances noise, and introduces boundary artifacts. The modifications incorporated in ASAHE suppress the artifacts and allow processing of chest radiographs. This dissertation describes the psychophysical methods used to evaluate the effects of processing algorithms on human observer performance. An experiment conducted with anthropomorphic phantoms and simulated nodules showed the ASAHE algorithm to be superior for human detection of nodules when compared to a computed radiography system's algorithm that is in current use. An experiment conducted using clinical images demonstrating pneumothoraces (partial lung collapse) indicated no difference in human observer accuracy when ASAHE images were compared to computed radiography images, but greater ease of diagnosis when ASAHE images were used. These results provide evidence to suggest that Artifact-Suppressed Adaptive Histogram Equalization can be effective in increasing diagnostic accuracy and efficiency.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectDissertations, Academic.en_US
dc.subjectRadiography -- Image quality.en_US
dc.subjectRadiography, Medical -- Digital techniques.en_US
dc.subjectRadiology.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineOptical Sciencesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorDallas, William J.en_US
dc.contributor.committeememberRoehrigm Hansen_US
dc.contributor.committeememberSchowengerdt, Roberten_US
dc.identifier.proquest9229840en_US
dc.identifier.oclc712680907en_US
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