Retinal Image Analysis

Jose Ignacio Orlando and Matthew B. Blaschko

Overview:

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.

Results:

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

Data:

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

References:

  1. Orlando, J. I. and M. B. Blaschko: Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2014. [bibtex; doi]
  2. J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B. van Ginneken, "Ridge based vessel segmentation in color images of the retina", IEEE Transactions on Medical Imaging, 2004, vol. 23, pp. 501-509.
  3. M. Niemeijer, J.J. Staal, B. van Ginneken, M. Loog, M.D. Abramoff, "Comparative study of retinal vessel segmentation methods on a new publicly available database", in: SPIE Medical Imaging, Editor(s): J. Michael Fitzpatrick, M. Sonka, SPIE, 2004, vol. 5370, pp. 648-656.
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