Abstract:
We have developed an algorithm for detection and classification of progressive retinal disease and normal subjects into their respective classes on the basis of drusen detection. This algorithm uses intensity based thresholding and poly fitting curve strategies for the purpose of drusen detection. The spectral domain OCT images dataset was used for cross validation consist on volumetric scans of dry ARMD affected and normal eyes named as 2014_BOE_Srinivasan - Modified2 dataset [1] of Duke University. This dataset consists on OCT volumetric scans: 15 patients each from normal and dry ARMD patients consist on 30 volumes. The proposed algorithm was successfully run on all normal OCT volumes and 12 out of 15 dry ARMD volumes. The proposed algorithm successfully classified 28 volumes out of 30 volumes with 92 % accuracy for all dry ARMD and Normal classes. The results indicate that proposed algorithm can be a supportive tool for early detection of dry ARMD retinal disease.
