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Proceedings Paper

Patch forest: a hybrid framework of random forest and patch-based segmentation
Author(s): Zhongliu Xie; Duncan Gillies
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Paper Abstract

The development of an accurate, robust and fast segmentation algorithm has long been a research focus in medical computer vision. State-of-the-art practices often involve non-rigidly registering a target image with a set of training atlases for label propagation over the target space to perform segmentation, a.k.a. multi-atlas label propagation (MALP). In recent years, the patch-based segmentation (PBS) framework has gained wide attention due to its advantage of relaxing the strict voxel-to-voxel correspondence to a series of pair-wise patch comparisons for contextual pattern matching. Despite a high accuracy reported in many scenarios, computational efficiency has consistently been a major obstacle for both approaches. Inspired by recent work on random forest, in this paper we propose a patch forest approach, which by equipping the conventional PBS with a fast patch search engine, is able to boost segmentation speed significantly while retaining an equal level of accuracy. In addition, a fast forest training mechanism is also proposed, with the use of a dynamic grid framework to efficiently approximate data compactness computation and a 3D integral image technique for fast box feature retrieval.

Paper Details

Date Published: 21 March 2016
PDF: 8 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978428 (21 March 2016); doi: 10.1117/12.2216365
Show Author Affiliations
Zhongliu Xie, Imperial College London (United Kingdom)
Duncan Gillies, Imperial College London (United Kingdom)

Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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