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@inproceedings{Xie:2016:10.1117/12.2216365,
author = {Xie, Z and Gillies, D},
doi = {10.1117/12.2216365},
publisher = {SPIE},
title = {Patch forest: A hybrid framework of random forest and patch-based segmentation},
url = {http://dx.doi.org/10.1117/12.2216365},
year = {2016}
}

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TY  - CPAPER
AB  - 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.
AU  - Xie,Z
AU  - Gillies,D
DO  - 10.1117/12.2216365
PB  - SPIE
PY  - 2016///
SN  - 1605-7422
TI  - Patch forest: A hybrid framework of random forest and patch-based segmentation
UR  - http://dx.doi.org/10.1117/12.2216365
ER  -

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