Multi-part segmentation for porcine offal inspection with auto-context and adaptive atlases

Stephen McKenna, Telmo Amaral, Thomas Plotz, Ilias Kyriazakis

Research output: Contribution to journalArticle

4 Citations (Scopus)
152 Downloads (Pure)

Abstract

Extensions to auto-context segmentation are proposed and applied to segmentation of multiple organs in porcine offal as a component of an envisaged system for post-mortem inspection at abbatoir. In common with multi-part segmentation of many biological objects, challenges include variations in configuration, orientation, shape, and appearance, as well as inter-part occlusion and missing parts. Auto-context uses context information about inferred class labels and can be effective in such settings. Whereas auto-context uses a fixed prior atlas, we describe an adaptive atlas method better suited to represent the multimodal distribution of segmentation maps. We also design integral context features to enhance context representation. These methods are evaluated on a dataset captured at abbatoir and compared to a method based on conditional random fields. Results demonstrate the appropriateness of auto-context and the beneficial effects of the proposed extensions for this application.
Original languageEnglish
Pages (from-to)290-296
Number of pages7
JournalPattern Recognition Letters
Volume112
Early online date30 Jul 2018
DOIs
Publication statusPublished - 1 Sep 2018

Keywords

  • Multi-class segmentation
  • Auto-context
  • Atlas-based segmentation
  • Automated inspection

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