Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
Model-Based Analysis of Medical Images
James S. Duncan, Ph.D.
Professor of Diagnostic Radiology and
Electrical Engineering
Director, Biomedical Engineering Program
Yale University
Monday, March 10, 2002, 4:15PM
TCSeq 201
http://robotics.stanford.edu/ba-colloquium/
Abstract
The development of methods to accurately and reproducibly recover
useful quantitative information from medical images is often hampered
by uncertainties in handling these data related to: image acquisition
parameters, the variability of normal human anatomy and physiology,
the presence of disease or other abnormal conditions, and a variety of
other factors. This talk will review image analysis strategies that
make use of models based on geometrical and physical/biomechanical
information to help constrain the range of possible solutions in the
presence of such uncertainty. The discussion will be focused by
looking primarily at three problem areas in the realms of cardiac
function analysis, neuroanatomical structure analysis and brain shift
issues in neurosurgery. In the cardiac area, we have developed a set
of algorithms that can derive in vivo strain patterns from noninvasive
four dimensional (3 spatial dimensions plus time) image datasets,
including Magnetic Resonance Imaging (MRI) and ultrasound. This
information is then used in conjunction with biomechanical models of
the LV to estimate dense strain patterns in the myocardium, which in
turn are used to study the physiology of the heart in both normal and
abnormal states. In the analysis of neuroanatomical structure, both
parametric and level set-based curve evolution strategies are used to
segment subcortical and cortical gray matter in the brain. In
addition, a variety of shape properties of these regions are used
along with volume estimates to study patient populations with a
variety of neurological disorders. Finally, we note that during
epilepsy neurosurgery, a multi-modality (MRI, MR spectroscopy, EEG,
SPECT) assortment of images and signals are acquired to give the
neurosurgeon a roadmap of the patient's brain in order to guide
surgical/interventional procedures aimed at eliminating severe seizure
activity. We have used a variety of mathematical reasoning strategies
to integrate or fuse this preoperative information. In addition, we
are currently working on developing a system that makes use of stereo
cameras mounted in the operating room, along with a linear elastic
biomechanical model of the brain, to account for the brain shift that
occurs during the surgery. Accurately accounting for this will greatly
improve the utility of our current image-guided surgery navigation
system.
The presentation will include a description of the problem areas and
visual examples of the image datasets being used, an overview of the
mathematical techniques involved and a presentation of results
obtained when analyzing actual patient image data using these
methods. Emphasis will be placed on how image-derived information and
appropriate modeling can be used together to address the image
analysis and processing problems noted above.
About the Speaker
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 Vice-Chair of Bioimaging Sciences
Research and the Director of the Image Processing and Analysis Group
within Diagnostic Radiology; and is the Chair of Yale's 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 objects from both 2D and 3D data, the
tracking of nonrigid object motion from 2D and 3D data, the use of
geometrical and phyiscal models to constrain the recovery of
information from images 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. He was
a Fulbright Research Scholar at the Universities of Amsterdam and
Utrecht in the Netherlands during part of the 1993-94 academic
year. In June, 1997, he chaired the international conference on
Information Processing in Medical Imaging (IPMI), held in Poultney,
Vermont. In 1999, he was elected as Fellow of the American Institute
for Medical and Biological Engineering (AIMBE) and in 2000 he was
elected to be a Fellow of the IEEE for contributions in medical image
analysis and computer vision.
Contact: bac-coordinators@cs.stanford.edu
Back to the Colloquium Page