Pre- and intra-operative image processing and visualization

Speaker:          Prof. William Wells   
                  Harvard Medical School, USA   

Title:            Pre- and intra-operative image processing and visualization 

Date:             Tuesday, 17 Feb 2004 

Time:             2:00pm - 3:00pm 

Venure:           Room 2404  
                  (Phase I, via lift nos. 17/18)
                  HKUST    


ABSTRACT: 

In this talk I will summarize pre-and intra-operative image processing
and visualization that are part of a research program in image-guided 
neurosurgery at Brigham and Women's Hospital. This program is focused 
on procedures in a .5 Tesla interventional MRI.  A central aspect of 
this work is the "3D Slicer", a freely available open source platform 
for medical image processing and visualization.

After describing these application projects, I will review some recent 
developments in statistical and information-theoretic image registration. 
The Mutual Information approach and related model-based methods will be 
described and contrasted, with the following observation:

If you can't move towards the known right thing, then you can at least 
move away from the most bad thing. In addition, recent experimental results 
will be described.


BIOGRAPHY: 

Prof. William M. Wells is a well-known researcher in medical image
processing.  He is a Associate Professor of Radiology at the Harvard
Medical School, and Brigham and Women's Hospital.  Prof. Wells is also an
Affiliated Faculty of the MIT Division of Health Sciences and Technology
in Harvard and the Research Scientist of MIT AI Laboratory.  Prof. Wells
earned his doctoral degree in 1992 during which time he worked with Prof.
Eric Grimson on research about Computerized Object Recognition.  He joined
the surgical planning laboratory at BWH in 1992 as postdoc.  Since that
time he has worked mostly on MRI segmentation and multi-modality
registration, primarily with methods of statistical image processing.  He
maintains a vigorous collaboration with the MIT AI laboratory faculty and
students.