Talk Title:  Segmentation and Measurement of Neuroanatomical Structure 
                    from Medical Images
        
Speaker: James S. Duncan, Ph.D.
         Image Processing and Analysis Group
         Yale University


Location: LG100, UF Brain Institute.

Date and Time: Tuesday, March 30, 7.00am




			 Abstract

In order to study a variety of neurological disorders, we have been
developing approaches to semi-automatically segment and measure both
subcortical and cortical neuroanatomical structure from Magnetic
Resonance (MR) images.  Our segmentation efforts have focused on the
use of two different types of deformable models, noting that i.) some
problems (e.g. many subcortical structures) are well suited to the
constraints that global shape information provides, where the shapes
of the structures are very consistent and are well characterized by a
specific shape model and ii.) other problems (e.g.  the cortical gray
matter layer) involve finding structures whose shapes are highly
variable or have little consistent shape at all and thus require more
generic constraints.  In this talk, we will discuss our approach to
both of these problems: in the first case using integrated approaches
in a maximum a posteriori formulation based on parametric models with
associated probability densities and in the second case, using level
set methods which incorporate powerful generic shape constraints, in
particular, a thickness constraint.

With regards to the measurement of this segmented structure, we will
also discuss a number of approaches aimed at moving beyond standard
volume measurement, especially in the cortex, to study the spatial
variation of parameters such as cortical thickness, shape, and sulcal
depth.  Quantitative results will be presented using both electronic
phantom data as well as patient data. The latter will include studies
of both normals and subjects with autism, one of the disorders under
investigation.




	                Bio-sketch


James S. Duncan received the BSEE degree from Lafayette College,
Easton, PA in 1973, the MS degree in Engineering from UCLA in 1975 and
the Ph.D.  degree in Electrical Engineering from the University of
Southern California, in 1982.

In 1973, he joined the staff of Hughes Aircraft Company,
Electro-Optical and Data Systems Group, and participated in research
and development projects related to signal and image processing for
forward looking infrared (FLIR) imaging systems until 1983. During
this time, he held Hughes' Masters, Engineer and Doctoral
Fellowships. In 1983, he joined the faculty of Yale University, New
Haven, CT., where he currently is a Professor of Diagnostic Radiology
and Electrical Engineering, is the Director of the Image Processing
and Analysis Group within Diagnostic Radiology and is the Director of
Undergraduate Studies for the Program in Biomedical Engineering.  His
research and teaching efforts have been in the areas of image
processing, computer vision and medical imaging. His current specific
research interests include the segmentation of deformable structure,
the characterization of nonrigid object motion/deformation using
geometrical and physical models, and the integration of processing
modules in vision systems, all with a special interest in using these
approaches for medical image analysis.

Dr. Duncan is a member of Eta Kappa Nu and Sigma Xi, is on the
editorial board of the Journal of Mathematical Imaging and Vision, and
is currently an Associate Editor for the IEEE Transactions on Medical
Imaging and a co- Editor of the journal  Medical Image
Analysis. In June, 1997, he chaired the international conference on
Information Processing in Medical Imaging (IPMI), held in
Poultney, Vermont.