High Level Mammographic Information Fusion For Real World Ontology Population
Yosra Ben Salem1, Rihab Idodi, Karim Saheb Ettabaa, Kamel Hamrouni1, Basel Solaiman
Journal of Digital Information Management, Vol. 15, No. 5, 2017.
DOI: 
Link: http://dline.info/fpaper/jdim/v15i5/jdimv15i5_3.pdf
Abstract:
In this paper, we propose a novel approach
for ontology instantiating from real data related to the
mammographic domain. In our study, we are interested in
handling two modalities of mammographic images
mammography and Breast MRI. Firstly, we propose to
model both images content in ontological representations
since ontologies allow the description of the objects from
a common perspective. In order, to overcome the
ambiguity problem of representation of image's entities,
we propose to take advantage of the possibility theory
applied to the ontological representation. Second, both
local generated ontologies are merged in a unique formal
representation with the use of two similarity measures:
syntactic measure and possibilistic measure. The
candidate instances are, finally, used for the global domain
ontology populating in order to empower the
mammographic knowledge base. The approach was
validated on real world domain and the results were
evaluated in terms of precision and recall by an expert.