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Automated Storage and Retrieval of Thin-Section CT Images to Assist Diagnosis: System Description and Preliminary Assessment¹

¹ From the Department of Radiology, Indiana University School of Medicine, UH 0279, 550 N University Blvd, Indianapolis, Indiana 46202 (A.M.A., H.W.M.); Department of Radiology, University of Wisconsin, Madison (L.S.B.); and Department of Electrical and Computer Engineering, Purdue University, West Lafayette, Ind (C.E.B., A.C.K., C.P., J.D., C.R.S., A.M.). Supported by National Science Foundation grant IRI9711535 and National Institutes of Health grant 1 RO1 LM06543-01A1. From the 2001 RSNA scientific assembly. Received February 28, 2002; revision requested April 30; final revision received September 20; accepted October 24. Address correspondence to A.M.A. (e-mail: aaisen@iupui.edu).

View larger version (44K): [in a new window]   Figure 1. Schematic shows the software steps and user interactions necessary to store an image in the database. Left: Procedures in a patient with a known diagnosis. Right: Procedures to retrieve images from the database that are similar to query images with unknown diagnoses. Periodically, the software determines which image properties to use for matching and retrieving images for each type of disease, as shown in the box at the bottom left. HRCT = high-resolution (thin-section) CT.

View larger version (118K): [in a new window]   Figure 2. Screen capture of user interface for phase 2 of the trial. User has identified and outlined a PBR. ASSERT software chose four images that contain similar-appearing abnormalities and displayed them, as well as the known diagnosis for these images (bronchiectasis). In the list of diagnostic possibilities, the user has made a selection.