A Comparative Study of Cell Nuclei Attributed Relational Graphs for Knowledge Description and Categorization in Histopathological Gastric Cancer Whole Slide Images

In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse...

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Bibliographic Details
Main Authors: Sharma, Harshita, Zerbe, Norman, Boger, Christine, Wienert, Stephan, Hellwich, Olaf, Hufnagl, Peter
Format: Conference Proceeding
Language:English
Subjects:
automatic classification
biomedical image analysis
Cancer
Cell nuclei attributed relational graphs
Computer architecture
digital histopathology
Feature extraction
gastric cancer
H&E stain
Image edge detection
Image segmentation
knowledge description
Microprocessors
Tumors
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Summary:In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification. A detailed comparative analysis of the proposed graph-based methods, also with the established low-level, object-level and high-level image descriptions is performed, that further leads to a hybrid approach combining salient visual information. Quantitative evaluation of investigated methods suggests the suitability of particular graph variants for automatic classification using H&E stained histopathological gastric cancer whole slide images based on HER2 immunohistochemistry.
ISSN:2372-9198
DOI:10.1109/CBMS.2017.25