Biomedical Engineering; Diagnostic Imaging; Magnetic Resonance Imaging; Radiology; Temporal Lobe
High Performance Computation
Dr. James Duncan works with engineering and mathematical principles and uses mainly signal/image processing techniques to derive useful image feature information and capture model-based information in concise mathematical forms. He uses nonlinear optimization methods to implement reasoning strategies. Dr. Duncan's image processing and analysis research can be divided into three general areas:
Segmentation of meaningful regions and/or objects in images. He looks at deformable model-based approaches to boundary finding and aim at locating the complete boundary of a deformable object efficiently and reproducibly. This is done by incorporating basic structural or parametric models into an optimization-based search strategy. Initially, Dr. Duncan aimed at 2D parametrized boundary finding, but now he has extended it into an approach for segmenting deformable surfaces from three-dimensional biomedical image data sets. Current applications include segmentation of the left ventricle of the heart from 4D cine Magnetic Resonance images (MRI) and segmentation of the temporal lobes of the brain from static MR images. Future research will focus on isolating structures using images obtained from laser-scanning confocal microscopes.
Image-based measurement and quantification of anatomical, physiological and/or clinically meaningful parameters. A primary example of this research is the tracking and modeling of non-rigid motion for the purpose of quantifying cardiac left ventricular (LV) regional function from 2-D and 3-D diagnostic image sequences. Such quantification permits measurements that are useful in understanding the basic relationships between the state of the heart muscle (myocardium) and overall LV function; these measurements can be important for managing patients with ischemic heart disease. Dr. Duncan's methodology makes use of mathematical models related to the motion of 3-D elastically deformable objects and is adaptable to the nonlinear, non-rigid regional motion of the LV.
Development of decentralized approaches for forming complete, integrated computer vision/image analysis systems. Dividing computer vision and image analysis systems into modular hierarchies is useful computationally and also allows to model particular image understanding tasks better. Hierarchical systems are necessary to perform such complex tasks as recognizing anatomy, quantifying the shape and motion of the heart, or performing an integrated segmentation of anatomical structures using multiple imaging modalities. He has recently developed an approach based on the mathematical concept of game theory, where functionals representing each module's tasks both compete and cooperate to make decisions about particular image analysis goals.
Specialized Terms: Computer vision; Image processing and medical imaging, with an emphasis on biomedical image analysis
Extensive Research Description
Dr. Duncan focuses on translating established or developing imaging technologies into quantitative-analysis applications. His group is entirely interdisciplinary. The projects evolve primarily from the intersection of researchers in other disciplines, who want to apply imaging technologies at the edges of the state of the art, with our abilities to push those technological limits. Some of the areas they currently work in are segmentation of deformable objects, image registration, measurement of neuroanatomical and cardiac function, strategies to track motion over time, image-guided neurosurgery and database search tools using images.
- S. Hadjidemetriou, D. Toomre and J.S. Duncan, "Motion tracking of the outer tips of microtubules," Medical Image Analysis, 2008. (electronic version online, in press).
- Y. Zhu X. Papademetris, and J. S. Duncan (2007) Segmentation of myocardial volumes from real-time 3D echocardiography using an incompressibility constraint.
- C. Delorenzo, X. Papademetris, K. Vives, D. Spencer and J. S. Duncan (2007) A comprehensive system for intraoperative 3D brain deformation recovery.
- C. DeLorenzo, X. Papademetris, L. H. Staib, K. P. Vives, D. D. Spencer and J. S. Duncan (2010) Image-guided intraoperative cortical deformation recovery using game theory: application to neocortical epilepsy surgery.
- P.Yan, N.Lin, A.Sinusas, J.S.Duncan, Boundary Element Method-Based Regularization For Recovery of LV Deformation, Medical Image Analysis, Vol. 11, No. 6, 2007. Pp. 540-554.