Wavelet transforms for detecting microcalcifications in mammography.

Persistent Link:
http://hdl.handle.net/10150/187416
Title:
Wavelet transforms for detecting microcalcifications in mammography.
Author:
Hahn, Hee Il.
Issue Date:
1995
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:
A three-stage method based on wavelet transforms for detecting and segmenting calcifications is developed. The first stage consists of preprocessing which is realized by subtracting from the original image a Gaussian low-pass filtered version, followed by a full resolution wavelet transform. With a Laplacian of Gaussian wavelet basis the detection of microcalcifications can be nearly optimized in the details sub-bands. In fact, the two-dimensional filters which transform the input image into HH and LH + HL details sub-bands are closely related to prewhitening matched filters for detecting Gaussian objects in separable and non-separable first-order Markov noise, respectively. Two methods have been proposed to detect candidate microcalcifications. In the first method, the outputs HH and LH + HL from each octave are thresholded at some fixed percentile of the histogram of each component. Then, the detected images from all octaves are logically ORed to yield the binary map of detected pixels. The second method employs a Hotelling observer. The Hotelling discriminant is computed and then thresholded to obtain the binary map. The second stage is needed to reduce false alarms, which are reduced by analyzing the shapes of the detected pixel regions. The third stage is designed to provide an accurate segmentation of calcification boundaries. Individual microcalcifications are often greatly enhanced in the output image. FROC curves are computed from tests using a well-known database of digitized mammograms. The algorithm using the Hotelling observer shows the best overall performance in which a true positive fraction of 73% is achieved at 0.7 false positives per image.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Electrical and Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Strickland, Robin N.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleWavelet transforms for detecting microcalcifications in mammography.en_US
dc.creatorHahn, Hee Il.en_US
dc.contributor.authorHahn, Hee Il.en_US
dc.date.issued1995en_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.abstractA three-stage method based on wavelet transforms for detecting and segmenting calcifications is developed. The first stage consists of preprocessing which is realized by subtracting from the original image a Gaussian low-pass filtered version, followed by a full resolution wavelet transform. With a Laplacian of Gaussian wavelet basis the detection of microcalcifications can be nearly optimized in the details sub-bands. In fact, the two-dimensional filters which transform the input image into HH and LH + HL details sub-bands are closely related to prewhitening matched filters for detecting Gaussian objects in separable and non-separable first-order Markov noise, respectively. Two methods have been proposed to detect candidate microcalcifications. In the first method, the outputs HH and LH + HL from each octave are thresholded at some fixed percentile of the histogram of each component. Then, the detected images from all octaves are logically ORed to yield the binary map of detected pixels. The second method employs a Hotelling observer. The Hotelling discriminant is computed and then thresholded to obtain the binary map. The second stage is needed to reduce false alarms, which are reduced by analyzing the shapes of the detected pixel regions. The third stage is designed to provide an accurate segmentation of calcification boundaries. Individual microcalcifications are often greatly enhanced in the output image. FROC curves are computed from tests using a well-known database of digitized mammograms. The algorithm using the Hotelling observer shows the best overall performance in which a true positive fraction of 73% is achieved at 0.7 false positives per image.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairStrickland, Robin N.en_US
dc.contributor.committeememberHuelsman, Lawrence P.en_US
dc.contributor.committeememberRodriguez, Jeffrey J.en_US
dc.contributor.committeememberMoloney, Jerryen_US
dc.identifier.proquest9622990en_US
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