Abstract
In this paper we describe mereotopological methods to programmatically correct image segmentation errors, in particular those that fail to fulfil expected spatial relations in digitised histological scenes. The proposed approach exploits a spatial logic called discrete mereotopology to integrate a number of qualitative spatial reasoning and constraint satisfaction methods into imaging procedures. Eight mereotopological relations defined on binary region pairs are represented as nodes in a set of 20 directed graphs, where the node-to-node graph edges encode the possible transitions between the spatial relations after set-theoretic and discrete topological operations on the regions are applied. The graphs allow one to identify sequences of operations that applied to regions of a given relation, and enables one to resegment an image that fails to conform to a valid histological model into one that does. Examples of the methods are presented using images of H&E-stained human carcinoma cell line cultures.
Highlights
IntroductionThis paper is an extended version of the work presented at the 21st Conference on Medical Image
This paper is an extended version of the work presented at the 21st Conference on Medical ImageUnderstanding and Analysis (MIUA 2017) [1] where we describe an application of mereotopological model-based methods for the algorithmic correction of segmentation errors
We look for resegmentation operations on candidate nucleus/cytoplasm pairs that take us from partially overlap (PO) to proper part (PP)
Summary
This paper is an extended version of the work presented at the 21st Conference on Medical Image. The analysis and model-based constraints are provided by a spatial logic called discrete meterotopology (DM) [2,3], which is used here to enhance classical imaging techniques and mathematical morphology (MM) operations This is achieved by explicitly encoding a number of binary relations such as contact, overlap, and the part–whole relation on pairs of image regions. Errors like this can—to a certain degree—be corrected by a process called resegmentation, where component regions are processed with, for example, MM operators or deformable models so that the expected spatial relation between them holds. While the potential of spatial reasoning in imaging has been suggested before (e.g., [4]), the novel aspects of our contribution are firstly on the role of mereotopology to enable systematic context-based processing of regions and their relations, and secondly their application to quantitative histological imaging where a systematic hierarchy of ontological levels [5] is essential to enable an in-depth interpretation of histological scene contents
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