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>>Hi I'm Anne Haake.
I'm a professor in the college
of Computing and Information
Sciences,
in the PhD department
and I'm in the Multidisciplinary
Vision Research Labs.
Content Based Image Retrieval
is a method of using
information in the image
itself.
Information such as color,
contrast, edges,
those types of features for
storing and retrieving images.
And it's a potential solution
to the problem of ever
increasing amounts of digital
images,
and the fact that most
of these images are not
annotated,
that is, there hasn't been
someone to add labels or tags
to those images.
And in biomedical area
one of the major uses
is to be able to retrieve
similar images
ok, for teaching, for research,
for diagnosis.
A current dermatology project
is to extend the traditional
content-based image retrieval to
what we're calling human
centered CBIR.
And we're using a couple
different techniques to try to
illicit domain knowledge from
our expert dermatologists.
We're doing eye tracking and
also having them do verbal
annotations as they're
describing these images, so that
we can understand their
perceptual expertise as well as
their conceptual knowledge.
The intent is to model the data
and to somehow bring all those
multimodal types of data
together to better organize the
images in new types of
databases.
>>We're in the image eye
tracking lab in the MVRL and
this is the set up that we use
for the human centered CBIR in
some of our experiments.
In the set up, the physician,
the dermatologist was seated in
front of the eye tracking
system. The eye tracker is below
the display that the physician
looked at.
Seated next to the physician
was the physician's assistant
who was listening to what the
physician is describing as she
provides the differential
diagnosis.
And the student was operating
the eye tracking system.
And so the physician would look
at the image and
develop the differential
diagnosis.
>>We're looking at the dorsal
hand of an African American
woman. She has
multiple-hyperkeratotic,
hyperpigmented papules
on her hand
and also a large plaque
may be a coalescence of multiple
papules.
>>By providing explorable
interfaces through natural
interaction,
and having databases that are
organized
according to expert knowledge,
it's our hope that doctors will
be able to find images
that are more relevant to their
needs.
Again, similar images for
teaching, variations, images
that resemble those of their
patient.
Our research project can serve
as a model for RIT's push
towards more Multidisciplinary
research.
And also RITs commitment
towards experiential learning
for students.
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