James Duncan: My research efforts have been in the areas of computer vision, image processing, and medical imaging, with an emphasis in biomedical image analysis. These efforts have included the segmentation of deformable structure from 3D image data, the tracking of non-rigid motion/deformation from spatiotemporal images and development of strategies for image-guided intervention/surgery. Frequently, the analysis strategies employ models based on geometrical and physical/biomechanical information to help constrain the range of possible solutions in the presence of uncertainty. My laboratory has focused on several problem areas within the realms of neuroimaging-based structure/function analysis, cardiac function analysis, and radiotherapy-based cancer treatment, along with some newer work in cellular and molecular image analysis from microscopy images. We strive to find unifying mathematical and algorithmic principles to address this range of problems.
James Duncan: Below are 3 examples of research performed in my laboratory:
- A level set-based strategy is used to segment neuroanatomical structure.
- Biomechanical models and Bayesian estimation are used to predict local left ventricular deformation.
- Spatial activation constraints are used to estimate fMRI activations.
Xenophon Papademetris: My research is currently focused on three main areas: (i) brain deformation modeling and compensation during neurosurgical interventions as well as complex 3D visualization of multimodal image data for surgical planning and guidance, (ii) Medical Image Analysis software development – primarily the BioImage Suite project – see www.bioimagesuite.org, and (iii) vascular image analysis both in terms of monitoring angiogenesis and the quantification of the growth of tissue engineered vessels.
Lawrence Staib: My work concerns the development and application of algorithms for the analysis of biomedical images for quantification of structure and function. Structural image analysis methods of interest include statistical and geometric deformable models for segmentation and model-based shape measurement and comparison. Diffusion weighted magnetic resonance image analysis also provides structural information and important problems here include white matter parcellation, quantification, and fiber tracking. Groups of subjects can be characterized both structurally and functionally by statistical characterization and classification using spatial patterns of structural and functional parameters.
Hemant Tagare: I work on theoretical and practical problems in bio-medical image analysis. My research focuses on image segmentation, non-rigid registration, 3D reconstruction, and shape theory. In segmentation, my research focuses on cardiac ultrasound and brain MRI segmentation, especially on the development of new methodology that draws on machine-learning and advanced numerical optimization techniques. In non-rigid registration, I am developing an axiomatic framework for registration which is applied to brain deformation and atlas building. In 3D reconstruction, my research focuses on reconstructing protein structure from Cryogenic Electron Microscopy. In shape theory, my work addresses questions of the topology and geometry of affine shape spaces and non-rigid shape comparison.