Lowell, J., Hunter, Andrew, Steel, D. , Basu, A., Ryder, R. and Kennedy, R. L. (2004) Measurement of retinal vessel widths from fundus images based on 2-D modeling. Medical Imaging, IEEE Transactions on, 23 (10). pp. 1196-1204.
Full content URL: http://dx.doi.org/10.1109/TMI.2004.830524
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Lowell2004TMIMeasurementOfRetinalVesselWidthsFromFundusImagesBasedOn2DModeling.pdf - Whole Document 409kB |
| Item Type: | Article |
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| Item Status: | Live Archive |
Abstract
Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel.
| Additional Information: | Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel. |
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| Keywords: | Retinal screening, computer vision |
| Subjects: | G Mathematical and Computer Sciences > G400 Computer Science G Mathematical and Computer Sciences > G740 Computer Vision |
| Divisions: | College of Science > School of Computer Science |
| ID Code: | 1216 |
| Deposited On: | 21 Sep 2007 |
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