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Hierarchical Segmentation Enhances Diagnostic Imaging Print E-mail
Dec 31 2007
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Product Outcome

With U.S. Food and Drug Administration clearance, BMI will sell its Med-Seg imaging system with the 2-D version of the RHSEG software, licensed from NASA in 2002 and featured in Spinoff 2004. The device is intended to analyze medical imagery from computed tomography (CT or CAT) scans, positron emission tomography (PET) scans, magnetic resonance imaging (MRI), ultrasound, digitized X-rays, digitized mammographies, dental Xrays, soft tissue analyses, and moving object analyses; the technology is also equipped to evaluate soft-tissue slides such as Pap smears for the diagnoses and management of diseases. The advanced image segmentations produced by the RHSEG software allow the Med-Seg system to bring out details in these tests not previously seen with the naked eye. This allows for quick and accurate diagnosis of diseases. Additionally, unlike some other image-analysis devices, Med-Seg does not manipulate the image, so what the physician sees is truly what is there, providing truer images than many other imaging techniques.

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The RHSEG software presents its results in a straightforward format consisting of a hierarchical set of image segmentations in either two or three spatial dimensions. This hierarchical presentation of results allows the user to choose the segmentation(s) of interest and to perform additional analyses.
The Med-Seg system reveals image properties not seen with even up-to-date image enhancement techniques. It is available with a custom data-mining server; a computational engine; and an object-oriented database to manage data, models, and results, and access BMI’s Hierarchical Image Segmentation Beowulf Cluster, an online problemsolving network.

By extending the software’s capabilities to three dimensions, BMI’s device may be able to produce a pixellevel view of all sides of a tumor or lesion. While current technology can produce 3-D imagery, the RHSEG software will be able to segment an image in ways that more clearly define problem areas. For example, the 3-D version of Med-Seg may be able to identify very early buildup of soft plaque within the arteries or identify density levels of microcalcification in mammographies, allowing physicians to diagnose malignant breast tumors well before they would normally be seen. In brain images, the physicians using Med-Seg will also be able to make earlier diagnoses of tumors or arteriovenous malformations.



 

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