This paper describes an application of topological, model-based methods for the algorithmic correction of segmentation errors in digitised histological images. The topological analysis is provided by the spatial logic Discrete Mereotopology and integrates qualitative spatial reasoning and constraint satisfaction methods with classical image processing methods. A set of eight topological relations defined on binary segmented regions are factored out and reworked as nodes of a set of directed graphs. The graphs encode and constrain a set of set-theoretic and topological segmentation operations on regions, so that the interpreted images and any proposed changes made to the regions can be made to conform to a valid histological model. Worked examples are given using images of H&E stained H400 cell line cultures.
|Title of host publication||Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings|
|Editors||Victor Gonzalez-Castro, Maria Valdes Hernandez|
|Number of pages||13|
|Publication status||Published - 22 Jun 2017|
|Event||21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom|
Duration: 11 Jul 2017 → 13 Jul 2017
|Name||Communications in Computer and Information Science|
|Conference||21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017|
|Period||11/07/17 → 13/07/17|
Bibliographical noteFunding Information:
This work was supported by the EPSRC through funding under grant EP/M023869/1, “Novel context-based segmentation algorithms for intelligent microscopy”.
- Graph theory
- Histological image processing