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Segmentation of organs in pig offal using auto-context

Lookup NU author(s): Dr Telmo Amaral, Professor Ilias Kyriazakis



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Institute of Electrical and Electronics Engineers, 2016.

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The segmentation of 2D images of 3D non-rigid objects into their constituent parts can pose challenging problems, such as missing and occluded parts, weak constraints over the spatial arrangement of parts, and variance in form and appearance. These problems have been addressed via segmentation methods that incorporate spatial context information, such as the auto-context technique. In this paper, we address for the first time the problem of segmenting multiple organs in images of pig offal, a challenging image analysis task that constitutes an essential step towards automated screening at abattoir for signs of sub-clinical diseases. We applied auto-context segmentation to a large data set of images and explored the effect of complementing conventional context features with integral features suited to our application.

Publication metadata

Author(s): Amaral T, Kyriazakis I, McKenna SJ, Plötz T

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE 13th International Symposium on Biomedical Imaging

Year of Conference: 2016

Pages: 1324-1328

Online publication date: 13/04/2016

Acceptance date: 22/12/2015

Date deposited: 24/03/2016

ISSN: 1945-7928

Publisher: Institute of Electrical and Electronics Engineers


DOI: 10.1109/ISBI.2016.7493511

Library holdings: Search Newcastle University Library for this item

ISBN: 19458452