Model-based Correction of Segmentation Errors in Digitised Histological Images
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Authors
Colleges, School and Institutes
External organisations
- University of Exeter
Abstract
This paper describes an application of model-based methods for the algorithmic correction of segmentation errors in digitised histo- logical images. This is a real-world application where qualitative spatial reasoning and constraint-satisfaction programming methods have been integrated with classical image processing methods to develop context- based histological imaging algorithms. The context here arises from: (i) making an ontological stand whereby regions rather than pixels in digi- tised images are deemed to be the main carriers of histological content, and (ii) highlighting the importance of and explicitly modelling topo- logical (and in particular relational) information encoded in digitised histological images. The topological analysis and representational frame- work used is provided by the spatial logic Discrete Meterotopology. We use this to augment classical Mathematical Morphology pixel and region- based operations by explicitly encoding sets of binary relations on pairs of regions such as contact, overlap and the part-whole relation. These mereotopological relations are used both to model the domain and to guide resegmentation algorithms so that our interpreted images conform to the requirements for a valid histological model.
Details
Original language | English |
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Title of host publication | Medical image understanding and analysis |
Subtitle of host publication | 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings |
Publication status | Published - 2017 |
Event | Medical Image Understanding and Analysis (MIUA) 2017 - Edinburgh, United Kingdom Duration: 11 Jul 2017 → 13 Jul 2017 https://miua2017.wordpress.com/ |
Publication series
Name | Communications in computer and information science |
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Publisher | Springer |
Volume | 723 |
ISSN (Print) | 1865-0929 |
ISSN (Electronic) | 1865-0937 |
Conference
Conference | Medical Image Understanding and Analysis (MIUA) 2017 |
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Country | United Kingdom |
Period | 11/07/17 → 13/07/17 |
Internet address |
Keywords
- Histological Image Processing, Mereotopology, Graph Theory