Work

Segmentation of organs in pig offal using auto-context

Year: 2016

Type: conference-paper

Abstract: 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.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi.2016.7493511

Authors: Telmo Amaral, Ilias Kyriazakis, Stephen J. McKenna, Thomas Plotz

Institutions: Newcastle University, University of Dundee

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Biotechnology and Biological Sciences Research Council

Award: 13TSB_AgriFood: Automated screening for pathologies at abattoir through computer vision- based inspection of pig carcasses