Cognitive Imaging: Using Knowledge Representation for Segmentation of MRA Data

This article, an extended version of ICCI*CC-2017 paper, co-authored by biomedical engineers specializing in brain blood circulation modeling and by experts in meaning-based NLP. This article suggests a cognitive computing technology for medical imaging analysis that removes image artifacts resulting in visual deviations from reality, such as discontinuous blood vessels or two vessels shown merged when they are not. It is implemented by supplying the pertinent knowledge that humans have to the computer and letting it initiate the corrective post-processing. The existing OST resource is centered on the ontology that is made to accommodate the domain with a minor adjustment effort; however, any ontology can be used, as demonstrated in this article. The examples from the ontology demonstrate the disparities between what the image shows and what the human knows. The computer detects them autonomously and can initiate the appropriate post-processing. If and when this cognitive imaging prevails, the post-processed images may replace the current ones as legitimate artifact-free MRIs.