June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Advanced retinal image analysis for AMD screening applications
Author Affiliations & Notes
  • Chaithanya Ramachandra
    Eyenuk Inc, Woodland hills, CA
  • Sandeep Bhat
    Eyenuk Inc, Woodland hills, CA
  • Malavika Bhaskaranand
    Eyenuk Inc, Woodland hills, CA
  • Muneeswar Gupta Nittala
    Doheny Eye Institute, Los Angeles, CA
  • Srinivas R Sadda
    Doheny Eye Institute, Los Angeles, CA
  • Kaushal Solanki
    Eyenuk Inc, Woodland hills, CA
  • Footnotes
    Commercial Relationships Chaithanya Ramachandra, Eyenuk, Inc. (E), Eyenuk, Inc. (P); Sandeep Bhat, Eyenuk, Inc. (E), Eyenuk, Inc. (P); Malavika Bhaskaranand, Eyenuk, Inc. (E); Muneeswar Nittala, Eyenuk, Inc. (F); Srinivas Sadda, Eyenuk, Inc. (F); Kaushal Solanki, Eyenuk, Inc. (E), Eyenuk, Inc. (P)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 3964. doi:
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      Chaithanya Ramachandra, Sandeep Bhat, Malavika Bhaskaranand, Muneeswar Gupta Nittala, Srinivas R Sadda, Kaushal Solanki; Advanced retinal image analysis for AMD screening applications. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):3964.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose
 

Age-related macular degeneration (AMD) is a progressive eye condition that results in loss of central vision and severely impacts quality of life for over 15 million elderly Americans. Grading of primary form of AMD (dry AMD) is done by quantifying area of drusen bodies, a process that is slow and error prone when done manually. To address this, we present a novel drusen detection and quantification scheme that is an essential first step to a fully-automated AMD screening system that can be applied to flag early signs of AMD.

 
Methods
 

Our technique combines robust low-level image processing and powerful statistical inference to enable fully-automated drusen identification from color fundus images. The steps include image normalization, region of interest detection, detected pixel description, pixel level classification, and blob level description and classification.<br /> We evaluate the method on a set of 40 images that were drusen annotated by experts at Doheny Eye Institute (DEI) in a region centered at the fovea with a radius of twice the ONH diameter. At each pixel the system evaluates low-level image properties (local edges, textures, brightness, contrast, color etc.) in a multi-scale framework, composes them into descriptors, classifies them using advanced machine learning techniques and aggregates the results.

 
Results
 

Fig. 1 presents an example of the detected drusen along with the drusen probability indicated by gray scale values. Blob level area under receiver operating characteristic curve (AUROC) for drusen identification on the entire set of images was evaluated to be 0.90 (Fig. 2).

 
Conclusions
 

We present a novel approach for drusen detection that achieves an AUROC of 0.90 on the test dataset and produces visualization with drusen probabilities that can be used for patient education. This would be a key component in a fully automated screening system that would aid early detection and treatment of AMD.  

 
Drusen detection example: Drusen probability for each detected blob is indicated by varying gray scale values (darker shades correspond to low probabilities; whiter shades correspond to high probabilities)
 
Drusen detection example: Drusen probability for each detected blob is indicated by varying gray scale values (darker shades correspond to low probabilities; whiter shades correspond to high probabilities)
 
 
Receiver operating characteristic (sensitivity-specificity curve) for drusen detection. AUROC = 0.90.
 
Receiver operating characteristic (sensitivity-specificity curve) for drusen detection. AUROC = 0.90.

 
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