Project Details


The fundamental objective of our proposed research project is to approach
biomedical image interpretation from a very new perspective: that of
knowledge-intensive experimental design of the segmentation process
itself. We use methods of artificial intelligence, specifically of
knowledge representation, diagnostic decision-making, planning and
learning, to carry out our objectives.

Our central hypothesis is simple: to make significant progress in
automating image recognition and measurement tasks we need to treat
recognition problems at the level of experimental design, so the best
solutions to various types of imaging problems can be derived by a
process of explicit specification, testing, and evaluation of different
segmentation strategies. We have already built a preliminary prototype
of the proposed system, and have tested it on brain lesion recognition
problems from multimodality magnetic resonance imaging (MRI). We are now
proposing to test both the methodological and practical assumptions
underlying the system. We will concentrate on automatic segmentation and
interpretation techniques for individual and serial MRI examinations,
which will be applied to automatically quantitate CNS changes in patients
with tumors, AIDS-related lesions, MS lesions, and other conditions.

The significance of this research for MR image interpretation lies in its
ability to provide both the clinical researcher and the laboratory
investigator the tools needed to carry out their work more efficiently
and effectively. In the clinical case we are focusing on the assessment
of volume changes in AIDS-related and other lesions to quantitate their
response to treatment, and in an industrial laboratory application the
quantitation of lesion volumes is also critical in assessing the
effectiveness of drugs undergoing testing. In both cases there is a
clear potential contribution to biomedical knowledge and future health
Effective start/end date8/15/938/14/99


  • National Center for Research Resources
  • National Center for Research Resources
  • National Center for Research Resources


  • Biochemistry, Genetics and Molecular Biology(all)
  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research


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