Fall / Winter 2012 - Rochester Institute of Technology

Focus Area | Human-Centered Computing
Human-Centered Computing
by William Dube
VIDEO LINK
A key problem in biomedical imaging is the difficulty of combining
human expertise with the power of advanced computing. Researchers
at RIT are attacking this problem by utilizing visual perception techniques,
computer modeling, and computational linguistics to infuse
human expertise into content-based image retrieval systems.
Creating Human-Centered Image Retrieval
Several years ago Anne Haake, professor in RIT’s Golisano
College of Computing and Information Sciences, undertook
a sabbatical at the National Library of Medicine that included
user research in Content-Based Image Retrieval (CBIR).
The computer-based technique, which catalogs and retrieves
images from a database of defined characteristics, is considered
a potential technical improvement over current image
databases used in medical diagnosis and prognosis.
“The images taken from an individual patient could be
compared to previous images taken by that medical center or
other centers around the world,” says Haake, who was trained
in biology and software development and now studies humancomputer
interaction and biomedical informatics. “Thousands
of images could be handled and analyzed quickly and previous
data about how tumors progress or how a particular disease may
look at different stages could be easily transmitted to doctors.”
However, in reviewing different CBIR systems as part of her
NLM research, Haake noted that the technology was hindered
because it did not effectively take into account human analysis
of the images themselves or the expertise of the analyst during
the early design stages.
Analyzing Biomedical Images:
The team is working with a
group of derma tologists to
develop a Human-Centered
CBIR system for dermatology
databases that has the
potential to greatly enhance
diagnosis and prognosis of
diseases from skin
conditions to cancer.
“CBIR assumes a certain level of uniformity in the viewer, that
every person looking at the image sees it the same way, uses the
same terminology to describe what they see, and has the same
level of expertise,” she adds. “However, this does not take into
account expert knowledge and past experience, which are crucial
in image semantics, making content retrieval and comparison
problematic at best and impossible in some cases.”
Haake has sought to address this issue by developing specific
models of human expertise and tacit knowledge that can
increase the overall “intelligence” of CBIR systems.
Through a multidisciplinary research team with imaging
scientist Jeff Pelz, computer scientist Pengcheng Shi, dermatologist
Cara Calvelli, and computational linguist Cecilia Ovesdotter
Master-Apprentice Interaction
During Image Inspection
Data Analysis
Outcome of Human-Defined
Perceptually Relevant
Image Decriptions
Monitor eye
movements
Fixation counts, durations
Region(s) of Interest
Expert
Novice
Capture
verbal
descriptions
Images
on ET Monitor
Transcription, NLP,
UMLS Mapping
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Hierarchy of concepts,
terms related to ROI
Hybrid Classifiers
Research at RIT
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