jim

Jim Gee

Jim’s major area of interest is biomedical image analysis and computing, with active research in all of the quantitative methods represented, including segmentation, registration, morphometry and shape statistics, as applied to a variety of organ systems and all of the major and emerging modalities in biological/biomaterials imaging and in vivo medical imaging.

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Traumatic Brain Injury

Traumatic Brain Injury

This collaboration with the Moss Rehabilitation Research Institute at the Albert Einstein Healthcare Network involves the development of methologies for examining both structural and connective properties in the brains of TBI survivors.
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Cardiac Medial Modeling

Cardiac Medial Modeling

The aims of this collaborative project with the Computational Imaging Lab at the Pompeu Fabra University are to use a medial model of the myocardium to generate stronger shape priors for segmentation, richer features for shape analysis and a shape-based … Continue reading
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Structure-Specific fMRI Analysis

Structure-Specific fMRI Analysis

We are developing a new class of techniques that focus statistical analysis of functional neuroimaging data on specific structures such as the hippocampus, using the shape of the structures as the guide by which to combine information across subjects in … Continue reading
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Multi-Atlas Segmentation

Multi-Atlas Segmentation

Objective Segmentation, the problem of locating and outlining objects of interest in images, is a central problem in biomedical image analysis. It is the primary mechanism for quantifying the properties of anatomical structures and pathological formations using complex imaging data. … Continue reading
  1. Yuanjie Zheng and Yan Wang and Brad M. Keller and Emily Conant and James C. Gee and Despina Kontos, “A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: Parameter optimization in a case-control breast cancer risk assessment study,” SPIE Medical Imaging 2013: Computer- Aided Diagnosis, 2013.
  2. Zheng, Y. and Lin, S. and Kang, S. and Xiao, R. and Gee, J. and Kambhamettu, C., “Single-Image Vignetting Correction from Gradient Distribution Symmetries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012.
  3. Keller, B.M. and Nathan, D.L. and Wang, Y. and Zheng, Y. and Gee, J.C. and Conant, E.F. and Kontos, D., “Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation.,” Medical physics, vol. 39, iss. 8, pp. 4903, 2012.
  4. Philip A Cook and Daniel C Alexander, “Inter-subject comparison of brain connectivity using Diffusion-Tensor Magnetic Resonance Imaging,” Medical Image Understanding and Analysis, pp. 117-120, 2003. [link to file]