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Proceedings Paper

Full automation of morphological segmentation of retinal images: a comparison with human-based analysis
Author(s): Mark P. Wilson; Shuyu Yang; Sunanda Mitra; Balaji Raman; Sheila Coyne Nemeth; Peter Soliz
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Paper Abstract

Age-Related Macular Degeneration (ARMD) is the leading cause of irreversible visual loss among the elderly in the US and Europe. A computer-based system has been developed to provide the ability to track the position and margin of the ARMD associated lesion; drusen. Variations in the subject's retinal pigmentation, size and profusion of the lesions, and differences in image illumination and quality present significant challenges to most segmentation algorithms. An algorithm is presented that first classifies the image to optimize the variables of a mathematical morphology algorithm. A binary image is found by applying Otsu's method to the reconstructed image. Lesion size and area distribution statistics are then calculated. For training and validation, the University of Wisconsin provided longitudinal images of 22 subjects from their 10 year Beaver Dam Study. Using the Wisconsin Age-Related Maculopathy Grading System, three graders classified the retinal images according to drusen size and area of involvement. The percentages within the acceptable error between the three graders and the computer are as follows: Grader-A: Area: 84% Size: 81%; Grader-B: Area: 63% Size: 76%; Grader-C: Area: 81% Size: 88%. To validate the segmented position and boundary one grader was asked to digitally outline the drusen boundary. The average accuracy based on sensitivity and specificity was 0.87 for thirty four marked regions.

Paper Details

Date Published: 15 May 2003
PDF: 11 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481388
Show Author Affiliations
Mark P. Wilson, Kestrel Corp. (United States)
Shuyu Yang, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)
Balaji Raman, Kestrel Corp. (United States)
Sheila Coyne Nemeth, Kestrel Corp. (United States)
Peter Soliz, Kestrel Corp. (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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