Full automation of morphological segmentation of retinal images - A comparison with human-based analysis

Mark Wilson, Shuyu Yang, Sunanda Mitra, Balaji Raman, Sheila Nemeth, Peter Soliz

Research output: Contribution to journalConference article

6 Scopus citations

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.

Original languageEnglish
Pages (from-to)27-37
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5032 I
DOIs
StatePublished - Sep 15 2003
EventMedical Imaging 2003: Image Processing - San Diego, CA, United States
Duration: Feb 17 2003Feb 20 2003

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Keywords

  • Age Related Macular Degeneration
  • Automatic Image Segmentation
  • Drusen Classification
  • Illumination Correction
  • Image Classification
  • Mathematical Morphology
  • Retinal Disease Screening System

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