Retinal Image Analysis

Jose Ignacio Orlando and Matthew B. Blaschko


In this work, we present a novel method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Retinal image analysis is greatly aided by blood vessel segmentation as the vessel structure may be considered both a key source of signal, e.g.\ in the diagnosis of diabetic retinopathy, or a nuisance, e.g.\ in the analysis of pigment epithelium or choroid related abnormalities. Blood vessel segmentation in fundus images has been considered extensively in the literature, but remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors such as a Potts model or total variation. In this work, we overcome this difficulty using a discriminatively trained conditional random field model with more expressive potentials. In particular, we employ recent results enabling extremely fast inference in a fully connected model. We find that this rich but computationally efficient model family, combined with principled discriminative training based on a structured output support vector machine yields a fully automated system that achieves results statistically indistinguishable from an expert human annotator.

Results obtained on healthy (top) and pathological (bottom) images from DRIVE.Scatter-plot of Se vs. Sp, comparing existing methods, local-neighborhood based CRF, the proposed method, and the human annotator, based on DRIVE data set.


Segmentations obtained on DRIVE test set [MICCAI 2014]: ZIP file here.


Our segmentation method was evaluated on DRIVE data set. It is available for download from the webpage:


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