Random forest active learning for retinal image segmentation

Abstract

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.