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ImageSURF: An ImageJ plugin for batch pixel-based image segmentation using random forests

Citation

O'Mara, A and King, AE and Vickers, JC and Kirkcaldie, MTK, ImageSURF: An ImageJ plugin for batch pixel-based image segmentation using random forests, Journal of Open Research Software, 5 Article 31. ISSN 2049-9647 (2017) [Refereed Article]


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Copyright Statement

2017 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.5334/jors.172

Abstract

Image segmentation is a necessary step in automated quantitative imaging. ImageSURF is a macro-compatible ImageJ2/FIJI plugin for pixel-based image segmentation that considers a range of image derivatives to train pixel classifiers which are then applied to image sets of any size to produce segmentations without bias in a consistent, transparent and reproducible manner. The plugin is available from ImageJ update site http://sites.imagej.net/ImageSURF/ and source code from https://github.com/omaraa/ImageSURF.

Item Details

Item Type:Refereed Article
Keywords:machine learning, image processing, pathology, ImageJ, segmentation, trainable segmentation, binary segmentation, random forests
Research Division:Psychology and Cognitive Sciences
Research Group:Cognitive Sciences
Research Field:Knowledge Representation and Machine Learning
Objective Division:Health
Objective Group:Clinical Health (Organs, Diseases and Abnormal Conditions)
Objective Field:Nervous System and Disorders
UTAS Author:O'Mara, A (Mr Aidan O'Mara)
UTAS Author:King, AE (Professor Anna King)
UTAS Author:Vickers, JC (Professor James Vickers)
UTAS Author:Kirkcaldie, MTK (Dr Matthew Kirkcaldie)
ID Code:123157
Year Published:2017
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2017-12-19
Last Modified:2018-07-24
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