Neighbourhood approximation using randomized forests.

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Title: Neighbourhood approximation using randomized forests.
Authors: Konukoglu, E
Glocker, B
Zikic, D
Criminisi, A
Item Type: Journal Article
Abstract: Leveraging available annotated data is an essential component of many modern methods for medical image analysis. In particular, approaches making use of the neighbourhood structure between images for this purpose have shown significant potential. Such techniques achieve high accuracy in analysing an image by propagating information from its immediate neighbours within an annotated database. Despite their success in certain applications, wide use of these methods is limited due to the challenging task of determining the neighbours for an out-of-sample image. This task is either computationally expensive due to large database sizes and costly distance evaluations, or infeasible due to distance definitions over semantic information, such as ground truth annotations, which is not available for out-of-sample images.This article introduces Neighbourhood Approximation Forests (NAFs), a supervised learning algorithm providing a general and efficient approach for the task of approximate nearest neighbour retrieval for arbitrary distances. Starting from an image training database and a user-defined distance between images, the algorithm learns to use appearance-based features to cluster images approximating the neighbourhood structured induced by the distance. NAF is able to efficiently infer nearest neighbours of an out-of-sample image, even when the original distance is based on semantic information. We perform experimental evaluation in two different scenarios: (i) age prediction from brain MRI and (ii) patch-based segmentation of unregistered, arbitrary field of view CT images. The results demonstrate the performance, computational benefits, and potential of NAF for different image analysis applications. © 2013 Elsevier B.V.
Issue Date: 10-May-2013
Start Page: 790
End Page: 804
Journal / Book Title: Med Image Anal
Volume: 17
Issue: 7
Copyright Statement: Copyright © 2013 Elsevier B.V. All rights reserved. NOTICE: this is the author’s version of a work that was accepted for publication in Medical Image Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Medical Image Analysis, 17(7), 2013. DOI:10.1016/
Conference Place: Netherlands
Appears in Collections:Computing

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