Patch forest: a hybrid framework of  random forest and patch-based segmentation

Zhongliu Xie; Duncan Gillies

doi:10.1117/12.2216365

21 March 2016 Patch forest: a hybrid framework of random forest and patch-based segmentation

Zhongliu Xie, Duncan Gillies

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978428 (2016) https://doi.org/10.1117/12.2216365
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Zhongliu Xie and Duncan Gillies "Patch forest: a hybrid framework of random forest and patch-based segmentation", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978428 (21 March 2016); https://doi.org/10.1117/12.2216365

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