AUTOMATED IMAGE ALIGNMENT AND SEGMENTATION TO FOLLOW PROGRESSION OF GEOGRAPHIC ATROPHY IN AGE-RELATED MACULAR DEGENERATION

作者：Ramsey David J; Sunness Janet S; Malviya Poorva; Applegate Carol; Hager Gregory D; Handa James T^*

来源：Retina-The Journal of Retinal and Vitreous Diseases, 2014, 34(7): 1296-1307.

DOI：10.1097/iae.0000000000000069

• age-related macular degeneration
• automated image segmentation
• fundus autofluorescence
• geographic atrophy

Purpose: To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA).
Methods: The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30 color fundus photographs for 10 patients.
Results: The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 +/- 5% sensitivity and 98 +/- 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 +/- 26% and specificity of 98 +/- 2%. The segmentation algorithm identified 75 +/- 16% of the GA border correctly in fundus autofluorescence images compared with just 42 +/- 25% for color fundus photographs.
Conclusion: The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.

• 出版日期2014-7