Manivannan, S;
Li, W;
Akbar, S;
Zhang, J;
Trucco, E;
McKenna, SJ;
(2016)
Local structure prediction for gland segmentation.
In: Kybic, J and Šonka, M, (eds.)
ISBI 2016: 13th International Symposium on Biomedical Imaging: Proceedings.
(pp. pp. 799-802).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
We present a method to segment individual glands from colon histopathology images. Segmentation based on sliding window classification does not usually make explicit use of information about the spatial configurations of class labels. To improve on this we propose to segment glands using a structure learning approach in which the local label configurations (structures) are considered when training a support vector machine classifier. The proposed method not only distinguishes foreground from background, it also distinguishes between different local structures in pixel labelling, e.g. locations between adjacent glands and locations far from glands. It directly predicts these label configurations at test time. Experiments demonstrate that it produces better segmentations than when the local label structure is not used to train the classifier.
| Type: | Proceedings paper |
|---|---|
| Title: | Local structure prediction for gland segmentation |
| Event: | ISBI 2016: 13th International Symposium on Biomedical Imaging, 13-16 April 2016, Prague, Czech Republic |
| ISBN-13: | 9781479923502 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISBI.2016.7493387 |
| Publisher version: | https://doi.org/10.1109/ISBI.2016.7493387 |
| Language: | English |
| Additional information: | Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | Glands, Image segmentation, Feature extraction, Support vector machines, Training, Colon, Image analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1535836 |
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