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Proceedings Paper

Performance assessment of multi-frequency processing of ICU chest images for enhanced visualization of tubes and catheters
Author(s): Xiaohui Wang; Mary E. Couwenhoven; David H. Foos; James Doran; David F Yankelevitz; Claudia I Henschke
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Paper Abstract

An image-processing method has been developed to improve the visibility of tube and catheter features in portable chest x-ray (CXR) images captured in the intensive care unit (ICU). The image-processing method is based on a multi-frequency approach, wherein the input image is decomposed into different spatial frequency bands, and those bands that contain the tube and catheter signals are individually enhanced by nonlinear boosting functions. Using a random sampling strategy, 50 cases were retrospectively selected for the study from a large database of portable CXR images that had been collected from multiple institutions over a two-year period. All images used in the study were captured using photo-stimulable, storage phosphor computed radiography (CR) systems. Each image was processed two ways. The images were processed with default image processing parameters such as those used in clinical settings (control). The 50 images were then separately processed using the new tube and catheter enhancement algorithm (test). Three board-certified radiologists participated in a reader study to assess differences in both detection-confidence performance and diagnostic efficiency between the control and test images. Images were evaluated on a diagnostic-quality, 3-megapixel monochrome monitor. Two scenarios were studied: the baseline scenario, representative of today's workflow (a single-control image presented with the window/level adjustments enabled) vs. the test scenario (a control/test image pair presented with a toggle enabled and the window/level settings disabled). The radiologists were asked to read the images in each scenario as they normally would for clinical diagnosis. Trend analysis indicates that the test scenario offers improved reading efficiency while providing as good or better detection capability compared to the baseline scenario.

Paper Details

Date Published: 6 March 2008
PDF: 8 pages
Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 691719 (6 March 2008); doi: 10.1117/12.770554
Show Author Affiliations
Xiaohui Wang, Carestream Health, Inc. (United States)
Mary E. Couwenhoven, Carestream Health, Inc. (United States)
David H. Foos, Carestream Health, Inc. (United States)
James Doran, Carestream Health, Inc. (United States)
David F Yankelevitz, Weill Cornell Medical College (United States)
Claudia I Henschke, Weill Cornell Medical College (United States)

Published in SPIE Proceedings Vol. 6917:
Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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