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UH Department of Biomedical Engineering Welcomes New Faculty Member

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Gifford
Gifford

Associate Professor Howard Gifford joins the UH Department of Biomedical Engineering from the University of Massachusetts Medical School. His research focuses on the medical imaging process with the goal of improving chances of detecting or quantifying a certain target. Specifically, Gifford works to optimize the performance of a task to read images, by examining hardware and image acquisition protocol as well.

"It's a question that has received a lot of attention recently because medical imaging is a burgeoning business," Gifford said. "There's new hardware being invented every day. What I'm interested in is trying to predict how a clinician, looking at an image, might perform given a particular imaging processing protocol."

One way of testing humans' response to medical images is to have them look at a batch of images and record what, if anything, they see. The downside to this process is that it's very time-consuming, and after a while, subjects tend to become fatigued when looking at images for research purposes such as this. As an alternative to this method, Gifford develops mathematical models, in lieu of human subjects, that will be used in the research process.

"We're trying to take it in a new direction by putting in some information about how humans respond to an image, such as examining what about the image that draws the observer's attention," said Gifford. "We're attempting to answer that by using eye-tracking techniques. If you follow the gaze of an observer and what it is that they're focusing on, it could be read as a false positive. You can see fixation points by looking at the tracking data, and you can measure the dwell time on a particular part of the image. Once you've acquired data, you can go back to your image and figure out what features of those fixation points might explain the data from your observer."

In correlating the tracking data with features of the image, Gifford builds that into the mathematical model to test. "If you can come up with a model that reliably predicts how humans will read an image, then you can use that model to explore comparisons of different hardware, perhaps different detector types that might be in development, and which one might be a better fit for a particular task," said Gifford.

Next semester, Gifford will teach a new graduate course on radiological image science. Course content will include an examination of the nature of photons that are used for x-ray and nuclear medicine imaging, the hardware components of acquiring data of detector types, reconstruction of images that a human can understand, and sources of noise artifacts in an image. Gifford received his Ph.D. in Applied Mathematics from the University of Arizona in 1997.

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