Maximin affinity learning of image segmentation

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Advances in Neural Information Processing Systems 22 (NIPS 2009). ;22

Maximin affinity learning of image segmentation Turaga Lab

Turaga Srinivas, Briggman Kevin, Helmstaedter Moritz, Denk Winfried, Seung Sebastian

Note: Research in this publication was not performed at Janelia.

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Abstract

Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are good in the sense of minimizing edge misclassification rates. However, this error measure is only indirectly related to the quality of segmentations produced by ultimately partitioning the affinity graph. We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well known segmentation performance measure. The Rand index measures segmentation performance by quantifying the classification of the connectivity of image pixel pairs after segmentation. By using the simple graph partitioning algorithm of finding the connected components of the thresholded affinity graph, we are able to train an affinity classifier to directly minimize the Rand index of segmentations resulting from the graph partitioning. Our learning algorithm corresponds to the learning of maximin affinities between image pixel pairs, which are predictive of the pixel-pair connectivity.

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Maximin affinity learning of image segmentation

Note: Research in this publication was not performed at Janelia.

Abstract

Janelia Authors