ImageSURF: An ImageJ Plugin for Batch Pixel-Based Image Segmentation Using Random Forests

Aidan O'Mara; Anna E. King; James C. Vickers; Matthew T. K. Kirkcaldie

doi:10.5334/jors.172

ImageSURF: An ImageJ Plugin for Batch Pixel-Based Image Segmentation Using Random Forests

Journal of Open Research Software

Volume 5 (2017): Issue 1

By: Aidan O'Mara, Anna E. King, James C. Vickers and Matthew T. K. Kirkcaldie

Open Access

|Nov 2017

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.

Funding statement: This research was supported by an Australian Government Research Training Program Scholarship.

References

Pham, D L, Xu, C and Prince, J L 2000 Current methods in medical image segmentation. Annual review of biomedical engineering, 2: 315–337. DOI: 10.1146/annurev.bioeng.2.1.315
Open DOI Search in Google Scholar Back to article
Roeder, A H K, Cunha, A, Burl, M C and Meyerowitz, E M 2012 A computational image analysis glossary for biologists. Development, 139(17): 3071–3080. DOI: 10.1242/dev.076414
Open DOI Search in Google Scholar Back to article
Sommer, C, Straehle, C, Kothe, U and Hamprecht, F A 2011 Ilastik: Interactive learning and segmentation toolkit. Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, 230–233. DOI: 10.1109/ISBI.2011.5872394
Open DOI Search in Google Scholar Back to article
Teverovskiy, M, Vengrenyuk, Y, Tabesh, A, Sapir, M, Fogarasi, S, Pang, H-Y, Khan, F M, Hamann, S, Capodieci, P and Clayton, M 2008 Automated localization and quantification of protein multiplexes via multispectral fluorescence imaging. Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on, 300–303. DOI: 10.1109/ISBI.2008.4540992
Open DOI Search in Google Scholar Back to article
Jucker, M 2010 The benefits and limitations of animal models for translational research in neurodegenerative diseases. Nature medicine, 16(11): 1210–1214. DOI: 10.1038/nm.2224
Open DOI Search in Google Scholar Back to article
Arganda-Carreras, I, Kaynig, V, Rueden, C T, Schindelin, J, Cardona, A and Seung, H S 2016 Trainable_Segmentation: Release v3.1.2. DOI: 10.5281/ZENODO.59290
Open DOI Search in Google Scholar Back to article
Schindelin, J, Rueden, C T, Hiner, M C and Eliceiri, K W 2015 The ImageJ Ecosystem: An Open Platform for Biomedical Image Analysis. Molecular Reproduction and Development, 82: 518–529. DOI: 10.1002/mrd.22489
Open DOI Search in Google Scholar Back to article
Breiman, L 2001 Random forests. Machine learning, 45(1): 5–32. DOI: 10.1023/A:1010933404324
Open DOI Search in Google Scholar Back to article
Frank, E, Hall, M A and Witten, I H 2016 The WEKA Workbench. Morgan Kaufmann, Fourth Edition.
Search in Google Scholar Back to article
Supek, F 2015 FastRandomForest. Google Code. Available at: https://code.google.com/archive/p/fast-random-forest/.
Search in Google Scholar Back to article
O’Mara, A R, Collins, J M, King, A E, Vickers, J C and Kirkcaldie, M T K 2017 ImageSURF MOAB2 Image Examples. DOI: 10.5281/zenodo.819511
Open DOI Search in Google Scholar Back to article
Rose, J and Goetz, B 2017 Minimal Value Types. Retrieved from: http://cr.openjdk.java.net/~jrose/values/shady-values.html May 2017.
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.5334/jors.172 | Journal eISSN: 2049-9647

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Language: English

Submitted on: Mar 28, 2017

Accepted on: Sep 29, 2017

Published on: Nov 6, 2017

Published by: Ubiquity Press

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

ImageJ,

FIJI,

segmentation,

trainable segmentation,

binary segmentation,

random forests

© 2017 Aidan O'Mara, Anna E. King, James C. Vickers, Matthew T. K. Kirkcaldie, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 5 (2017): Issue 1