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Proceedings Paper

Motion blur detection in radiographs
Author(s): Hui Luo; William J. Sehnert; Jacquelyn S. Ellinwood; David Foos; Bruce Reiner; Eliot Siegel
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Paper Abstract

Image blur introduced by patient motion is one of the most frequently cited reasons for image rejection in radiographic diagnostic imaging. The goal of the present work is to provide an automated method for the detection of anatomical motion blur in digital radiographic images to help improve image quality and facilitate workflow in the radiology department. To achieve this goal, the method first reorients the image to a predetermined hanging protocol. Then it locates the primary anatomy in the radiograph and extracts the most indicative region for motion blur, i.e., the region of interest (ROI). The third step computes a set of motion-sensitive features from the extracted ROI. Finally, the extracted features are evaluated by using a classifier that has been trained to detect motion blur. Preliminary experiments show promising results with 86% detection sensitivity, 72% specificity, and an overall accuracy of 76%.

Paper Details

Date Published: 11 March 2008
PDF: 8 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69140U (11 March 2008); doi: 10.1117/12.770613
Show Author Affiliations
Hui Luo, Carestream Health, Inc. (United States)
William J. Sehnert, Carestream Health, Inc. (United States)
Jacquelyn S. Ellinwood, Carestream Health, Inc. (United States)
David Foos, Carestream Health, Inc. (United States)
Bruce Reiner, Univ. of Maryland (United States)
Eliot Siegel, Univ. of Maryland (United States)

Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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