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We present a new variational model for saliency detection in images and its application to brain tumor segmentation. Incorporating a saliency term to a classical Total Variation based restoration functional this model is able to discriminate what is relevant (salient) from the background, resulting into a non-convex and non-smooth problem. To optimize the parameters of the proposed energy functional we introduce a Deep Learning framework using available knowledge from the specific application.
This presentation is part of Minisymposium “MS5 - Learning and adaptive approaches in image processing (2 parts)”
organized by: Kostas Papafitsoros (Weierstrass Institute Berlin) , Michael Hintermüller (Humboldt University and Weierstrass Institute Berlin) .