Topographic classification of nuclear medicine images for tumor detection

Persistent Link:
http://hdl.handle.net/10150/278117
Title:
Topographic classification of nuclear medicine images for tumor detection
Author:
Nguyen, Son Hung, 1966-
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:
Topographic classification is a nonlinear technique used to enhance nuclear medicine images for tumor detection. Second-order directional derivatives are computed at each pixel location after performing a least-squares fit of the underlying surface using a bivariate cubic polynomial. The eigenvalues and their corresponding eigenvectors computed from the Hessian matrix determine which topographic feature is assigned to the image pixel. Parameter selection for the mask size, curvature threshold, and angle thresholds are chosen to yield the "best" classified image. The classifier is applied to clinical images of cancer patients provided by the Department of Nuclear Medicine at the University of Arizona. Background noise associated with the photon-starved data is suppressed using a Difference-of-Gaussians (DOG) filter prior to pixel classification. Results indicate the feasibility of using this technique to isolate possible tumor sites which will assist the clinician during patient examination.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Health Sciences, Radiology.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College
Degree Grantor:
University of Arizona
Advisor:
Strickland, Robin N.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleTopographic classification of nuclear medicine images for tumor detectionen_US
dc.creatorNguyen, Son Hung, 1966-en_US
dc.contributor.authorNguyen, Son Hung, 1966-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.abstractTopographic classification is a nonlinear technique used to enhance nuclear medicine images for tumor detection. Second-order directional derivatives are computed at each pixel location after performing a least-squares fit of the underlying surface using a bivariate cubic polynomial. The eigenvalues and their corresponding eigenvectors computed from the Hessian matrix determine which topographic feature is assigned to the image pixel. Parameter selection for the mask size, curvature threshold, and angle thresholds are chosen to yield the "best" classified image. The classifier is applied to clinical images of cancer patients provided by the Department of Nuclear Medicine at the University of Arizona. Background noise associated with the photon-starved data is suppressed using a Difference-of-Gaussians (DOG) filter prior to pixel classification. Results indicate the feasibility of using this technique to isolate possible tumor sites which will assist the clinician during patient examination.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectHealth Sciences, Radiology.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorStrickland, Robin N.en_US
dc.identifier.proquest1348484en_US
dc.identifier.bibrecord.b27583697en_US
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