Document Type
Thesis
Date of Degree
Spring 2010
Degree Name
MS (Master of Science)
Degree In
Biomedical Engineering
First Advisor
Michael D. Abràmoff
Second Advisor
Joseph M. Reinhardt
First Committee Member
Michael D. Abramoff
Second Committee Member
Joseph M. Reinhardt
Third Committee Member
Edwin L. Dove
Abstract
Automated localization of the optic disc and fovea are important in the field of analysis of fundus images. We introduce a simultaneous detection method for optic disc and fovea with an enhancement and correction step. In the first step, a set of features are extracted from the color fundus image, and the relationship between the feature set and a distance variable d is established during training phase. For a test image, the same set of features is measured and the distance to the optic disc and fovea can be estimated using k-nearest-neighbor regression. A probability image is generated during this step. In the second, a second k-nearest-neighbor regression is applied on the probability image. Detected high likelihood regions from the first step can be enhanced only if they satisfy the trained relationship. The detected regions that do not get support from the other detected structure will be suppressed. 150 color fundus images were used to train the system. 50 color fundus images were used to test the system. The distance error for the optic disc is 9.8±8.3 pixels. The distance error for the fovea is 13.7±6.6 pixels.
Pages
xi, 63 pages
Bibliography
Includes bibliographical references (pages 62-63).
Copyright
Copyright 2010 Xiayu Xu
Comments
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