Abstract:
This thesis addresses ontology-driven prognosis assistance using knowledge
representation and reasoning for very large microscopic medical images. One
particular medical application in which prognosis assistance is needed is the
breast cancer grading. Although this is considered the key assessment tool in
prognosis of modern pathology practice, time constraints, the intra-observer
reproducibility and the inter-observer reliability inconsistencies determine a
lack of consensus which emphasizes the subjectivity of the method. To this
end, we propose a qualitative formal ontological representation of breast
cancer grading, an application ontology entitled Breast Cancer Grading
Ontology based on OWL-DL and SWRL formalisms. By this approach, the
thesis also tackles the semantic gap between the high- level semantic
concepts and the low-level image features. Additionally, we propose a spatial
theory support for the representation of the spatial relations between the
spatial concepts of the breast cancer grading. This ontology is integrated into
a cognitive microscope framework MICO, guiding the image exploration,
semantic indexing and retrieval of the microscopic images.
