Abstract:
In biological imaging the data is often represented by a sequence of anisotropic frames - the resolution in one dimension is significantly lower than in the other dimensi...Show MoreMetadata
Abstract:
In biological imaging the data is often represented by a sequence of anisotropic frames - the resolution in one dimension is significantly lower than in the other dimensions. E.g. in electron microscopy it arises from the thickness of a scanned section. This leads to blurred images and raises problems in tasks like neuronal image segmentation. We present an approach called SuperSlicing to decompose the observed frame into a sequence of plausible hidden sub-frames. Based on sub-frame decomposition by SuperSlicing we propose a novel automated method to perform neuronal structure segmentation. We test our approach on a popular benchmark, where SuperSlicing preserves topological structures significantly better than other algorithms.
Date of Conference: 29 April 2014 - 02 May 2014
Date Added to IEEE Xplore: 31 July 2014
Electronic ISBN:978-1-4673-1961-4