In this dissertation, I investigated computing algorithms for automated retinal blood
vessel detection. Changes in blood vessel structures are important indicators of many
diseases such as diabetes, hypertension, etc. Blood vessel is also very useful in tracking of
disease progression, and for biometric authentication. In this dissertation, I proposed two
algorithms to detect blood vessel maps in retina. The first algorithm is based on integration
of a Gaussian tracing scheme and a Gabor-variance filter. This algorithm traces the large
blood vessel in retinal images enhanced with adaptive histogram equalization. Small
vessels are traced on further enhanced images by a Gabor-variance filter. The second
algorithm is called a radial contrast transform (RCT) algorithm, which converts the
intensity information in spatial domain to a high dimensional radial contrast domain.
Different feature descriptors are designed to improve the speed, sensitivity, and
expandability of the vessel detection system. Performances comparison of the two
algorithms with those in the literature shows favorable and robust results. Furthermore, a new performance measure based on central line of blood vessels is proposed as an
alternative to more reliable assessment of detection schemes for small vessels, because the
significant variations at the edges of small vessels need not be considered.
The proposed algorithms were successfully tested in the field for early diabetic
retinopathy (DR) screening. A highly modular code library to take advantage of the parallel
processing power of multi-core computer architecture was tested in a clinical trial.
Performance results showed that our scheme can achieve similar or even better
performance than human expert readers for detection of micro-aneurysms on difficult
images.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2914 |
| Date | 15 May 2009 |
| Creators | Zhang, Ming |
| Contributors | Liu, Jyh-Charn |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
Page generated in 0.0025 seconds