Development, implementation and evaluation of segmentation algorithms for the automatic classification of cervical cells
department or school: Science, Faculty of
department or school: Physics and Astronomy, Department of
data provider: DSpace
degree campus: UBCV
creator: MacAulay, Calum Eric
issued: 2010-10-14T22:01:42Z
issued: 1989
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Cancer of the uterine cervix is one of the most common cancers in women. An effective screening program for pre-cancerous and cancerous lesions can dramatically reduce the mortality rate for this disease. In British Columbia where such a screening program has been in place for some time, 2500 to 3000 slides of cervical smears need to be examined daily. More than 35 years ago, it was recognized that an automated pre-screening system could greatly assist people in this task. Such a system would need to find and recognize stained cells, segment the images of these cells into nucleus and cytoplasm, numerically describe the characteristics of the cells, and use these features to discriminate between normal and abnormal cells. The thrust of this work was 1) to research and develop new segmentation methods and compare their performance to those in the literature, 2) to determine dependence of the numerical cell descriptors on the segmentation method used, 3) to determine the dependence of cell classification accuracy on the segmentation used, and 4) to test the hypothesis that using numerical cell descriptors one can correctly classify the cells. The segmentation accuracies of 32 different segmentation procedures were examined. It was found that the best nuclear segmentation procedure was able to correctly segment 98% of the nuclei of a 1000 and a 3680 image database. Similarly the best cytoplasmic segmentation procedure was found to correctly segment 98.5% of the cytoplasm of the same 1000 image database. Sixty-seven different numerical cell descriptors (features) were calculated for every segmented cell. On a database of 800 classified cervical cells these features when used in a linear discriminant function analysis could correctly classify 98.7% of the normal cells and 97.0% of the abnormal cells. While some features were found to vary a great deal between segmentation procedures, the classification accuracy of groups of features was found to be independent of the segm…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/29184
note: DEVELOPMENT, IMPLEMENTATION AND EVALUATION OF SEGMENTATION ALGORITHMS FOR THE AUTOMATIC CLASSIFICATION OF CERVICAL CELLS by Cal vim Eric MacAulay B.'Sc, Dalhousie University, 1982 M.Sc, Dalhousie University, 1984 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES Department of Physics We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA August 1989 ©Calum Eric MacAulay, 1989 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of The University of British Columbia Vancouver, Canada •ate fr^// / f t ? DE-6 (2/88) ABSTRACT Cancer of the uterine cervix is one of the most common cancers in women. An effective screening program for pre-cancerous and cancerous lesions can dramatically reduce the mortality rate for this disease. In British Columbia where such a screening program has been in place for some time, 2500 to 3000 slides of cervical smears need to be examined daily. More than 35 years ago, i t was recognized that an automated pre-screening system could greatly assist people in this task. Such a system would need to find and recognize stained cel l s , segment the images of these cells into nucleus and cytoplasm, numerically describe the characteristics of the cells, and use these features to discriminate between normal and abnormal cel l s . The thrust of this work was 1) to research and develop new segmentation methods and compare their performance to those in the literature,…
has type: Thesis/Dissertation
is shown at: 10.14288/1.0085030
language: eng
degree discipline: Physics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/29184