It all started more than 25 years ago, when James C. Tilton, a scientist at NASA's Goddard Space Flight Center, began investigating a novel way to analyze the pixels that comprise digital images.
He devised an algorithm that took image segmentation (grouping pixels at different levels of detail) to a whole new level; he not only found regional objects, but also grouped spatially separate objects into region classes. In other words, applied to a satellite image, it could not only identify and separate lakes of varying depths, but could recognize lakes as a class of objects spatially distinguishable from, say, trees.
He calls this Recursive Hierarchical Segmentation, and it has been used to analyze Earth-imaging data from NASA's Landsat and Terra spacecraft to improve snow and ice maps, find potential locations for archeological digs, etc. It is now being applied to medical imaging to improve mammograms, ultrasounds, digital x-rays, and more.
"My original concept was geared to Earth science," says Tilton, who was at first skeptical that his algorithm could enhance, say, mammography. "I never thought it would be used for medical imaging."
Then he processed cell images and saw details not visible in unprocessed displays of those images. "The cell features stood out real clearly, and this made me realize that Bartron was onto to something."
Bartron Medical Imaging, based out of Connecticut, has since developed the new MED-SEG system, which the FDA recently cleared for use by trained professionals to process images alongside other images, though stipulated that the system should not (at least yet) be used for primary image diagnosis.
Bartron, which first studied the software through Goddard's Innovative Partnerships Program Office, licensed the patented technology in 2003 to create a system that would differentiate hard-to-see medical image details. It then began to work with doctors to analyze CT scans, MRIs, ultrasounds, etc.… Read more
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