Random Forest Active Learning for Retinal Image Segmentation

  • Borja AyerdiEmail author
  • Manuel Graña
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)


Computer-assisted detection and segmentation of blood vessels in retinal images of pathological subjects is difficult problem due to the great variability of the images. In this paper we propose an interactive image segmentation system using active learning which will allow quick volume segmentation requiring minimal intervention of the human operator. The advantage of this approach is that it can cope with large variability in images with minimal effort. The collection of image features used for this approach is simple statistics and undirected morphological operators computed on the green component of the image. Image segmentation is produced by classification by a random forest (RF) classifier. An initial RF classifier is built from seed set of labeled points. The human operator is presented with the most uncertain unlabeled voxels to select some of them for inclusion in the training set, retraining the RF classifier. We apply this approach to a well-known benchmarking dataset achieving results comparable to the state of the art in the literature.


Image Segmentation Random Forest Matched Filter Random Forest Classifier Active Learning Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been partially funded by Basque Government grant IT874-13 for the research group. Manuel Graña was supported by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE European Research Centre of Network Intelligence for Innovation Enhancement.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of CCIAComputer Intelligence Group, UPV/EHUSan SebastianSpain
  2. 2.ENGINE Centre, Wrocław University of TechnologyWrocławPoland

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