My main research area is computational image analysis with an emphasis
on studying statistical models from a Bayesian perspective. My research
covers object localization and recognition, data alignment, and shape
representation, with a particular focus on neuroimaging. It is my long
term goal to enhance patient care by creating algorithms for
automatically quantifying and generalizing the information latent in
images for tasks such as disease analysis and surgical planning.
K.M. Pohl, R. Kikinis, and W.M. Wells. Active mean
fields: Solving the mean field approximation in the level set
framework. In Information Processing in Medical Imaging, vol. 4584 of
Lecture Notes in Computer Science, pages 26-37. Springer-Verlag, 2007.
[2]
K.M. Pohl, J. Fisher, S. Bouix, M.E. Shenton, R.W. McCarley,
W.E.L. Grimson, R. Kikinis, and W.M. Wells.
Using the logarithm of odds to define a vector space on
probabilistic atlases. Medical Image Analysis, 11(6),
pp. 465-477, 2007
[3]
K.M. Pohl, S. Bouix, M. Nakamura, T. Rohlfing,
R.W. McCarley, R. Kikinis, W.E.L. Grimson, M.E. Shenton,
and W.M. Wells. A hierarchical algorithm for MR brain image parcellation.
IEEE Transactions on Medical Imaging, 26(9),pp 1201-1212, 2007
[4]
K. M. Pohl, J. Fisher, W.E.L. Grimson,
R. Kikinis, and W.M. Wells. A Bayesian model for joint
segmentation and registration. NeuroImage,
31(1), pp. 228-239, 2006
