BREAST TISSUE CLASSIFICATION USING STATISTICAL PATTERN RECOGNITION ON BACKSCATTERED ULTRASOUND.

Persistent Link:
http://hdl.handle.net/10150/187672
Title:
BREAST TISSUE CLASSIFICATION USING STATISTICAL PATTERN RECOGNITION ON BACKSCATTERED ULTRASOUND.
Author:
BLEIER, ALAN RAYMOND.
Issue Date:
1984
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:
Diagnoses using images made with non-ionizing ultrasound are based on qualitive criteria and are not more accurate than those made with mammography. Information about tissue state is lost in the processing required to produce ultrasound images, and textural information may not be perceptible to a human observer. This study uses statistical pattern recognition to classify ultrasound A-scans, before any processing other than amplification occurs. A U. I. Octoson was used to collect data from normal, benign, and malignant, in vivo breast tissues. Features based on textural or frequency content of received sound were computed from digitized A-scans. Most textural features have been used previously in image processing, while frequency features assumed differences in frequency-dependent attenuation. Data were collected at the University of Arizona from 17 malignant masses, 8 benign masses, and 7 normal tissues. Univariate and multivariate statistical tests were used to find combinations of features which discriminated best between the classes of tissue. Equal a priori probabilities were used in a Bayesian classifier to classify malignant vs. nonmalignant. Specificity of 76% (13 of 17 malignant masses correct) was found with a sensitivity of 80% (12 of 15 masses correct). A linear combination of one frequency feature and three textural features was used. For malignant vs. benign, sensitivity of 88% (15 of 17 masses) and specificity of 75% (6 of 8 masses) were found. Features used were the same as for classification of malignant vs. nonmalignant, except for modification of one textural feature. The inability to visually detect and gather data from some palpable masses means that further study is needed to determine the effectiveness of applying the method to all breast masses. A set of A-scans from Thomas Jefferson Hospital in Philadelphia was gathered using similar procedures, and analysed with the following results: 18 of 21 (86%) malignant masses, and 45 of 66 (68%) nonmalignant masses were classified correctly, using a linear combination of one textural feature and five frequency features. Confidence limits on the results show that the majority of masses can be classified correctly with this procedure, but success rates are not high enough for breast cancer screening.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Breast -- Cancer -- Diagnosis.; Breast -- Examination.; Diagnostic ultrasonic imaging.; Ultrasonic imaging.; Ultrasonics in medicine.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Optical Sciences; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Swindell, William

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleBREAST TISSUE CLASSIFICATION USING STATISTICAL PATTERN RECOGNITION ON BACKSCATTERED ULTRASOUND.en_US
dc.creatorBLEIER, ALAN RAYMOND.en_US
dc.contributor.authorBLEIER, ALAN RAYMOND.en_US
dc.date.issued1984en_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.abstractDiagnoses using images made with non-ionizing ultrasound are based on qualitive criteria and are not more accurate than those made with mammography. Information about tissue state is lost in the processing required to produce ultrasound images, and textural information may not be perceptible to a human observer. This study uses statistical pattern recognition to classify ultrasound A-scans, before any processing other than amplification occurs. A U. I. Octoson was used to collect data from normal, benign, and malignant, in vivo breast tissues. Features based on textural or frequency content of received sound were computed from digitized A-scans. Most textural features have been used previously in image processing, while frequency features assumed differences in frequency-dependent attenuation. Data were collected at the University of Arizona from 17 malignant masses, 8 benign masses, and 7 normal tissues. Univariate and multivariate statistical tests were used to find combinations of features which discriminated best between the classes of tissue. Equal a priori probabilities were used in a Bayesian classifier to classify malignant vs. nonmalignant. Specificity of 76% (13 of 17 malignant masses correct) was found with a sensitivity of 80% (12 of 15 masses correct). A linear combination of one frequency feature and three textural features was used. For malignant vs. benign, sensitivity of 88% (15 of 17 masses) and specificity of 75% (6 of 8 masses) were found. Features used were the same as for classification of malignant vs. nonmalignant, except for modification of one textural feature. The inability to visually detect and gather data from some palpable masses means that further study is needed to determine the effectiveness of applying the method to all breast masses. A set of A-scans from Thomas Jefferson Hospital in Philadelphia was gathered using similar procedures, and analysed with the following results: 18 of 21 (86%) malignant masses, and 45 of 66 (68%) nonmalignant masses were classified correctly, using a linear combination of one textural feature and five frequency features. Confidence limits on the results show that the majority of masses can be classified correctly with this procedure, but success rates are not high enough for breast cancer screening.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectBreast -- Cancer -- Diagnosis.en_US
dc.subjectBreast -- Examination.en_US
dc.subjectDiagnostic ultrasonic imaging.en_US
dc.subjectUltrasonic imaging.en_US
dc.subjectUltrasonics in medicine.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.advisorSwindell, Williamen_US
dc.identifier.proquest8415044en_US
dc.identifier.oclc690936379en_US
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