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Proceedings Paper

Spatially varying scatter compensation for chest radiographs using a hybrid Madaline artificial neural network
Author(s): Joseph Y. Lo; Alan H. Baydush; Carey E. Floyd
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Paper Abstract

We developed a hybrid artificial neural network for scatter compensation in digital portable chest radiographs. The network inputs an image region of interest (ROI), and outputs the scatter estimate at the ROI's center. We segmented each image into four regions by relative detected exposure, then trained a separate Adaline (adaptive linear element) or adaptive filter for each region. We produced a spatially varying hybrid Madaline (mulitple Adaline) by combining outputs from weight matrices of different sizes trained for different durations. The network was trained with 20 patient or 1280 examples, then evaluated with another 5 patients or 320 examples. Scatter estimation errors were not very different, ranging from the Adaline's 6.9 percent to the hybrid Madaline's 5.5 percent. Primary errors (more relevant to quantitative radiography techniques like dual energy imaging) were 43 percent for the Adaline, reduced to 27 percent for the Madaline, and further reduced to 19 percent for the hybrid Madaline. The trained weight matrices, which act like convolution filters, resembled the shape and magnitude of scatter point spread functions. All networks outperformed conventional convolution-subraction techniques using analytical kernels. With its spatially varying neural network model, the hybrid Madaline provided the most accurate and robust estimation of scatter and primary exposures.

Paper Details

Date Published: 11 May 1994
PDF: 11 pages
Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); doi: 10.1117/12.175095
Show Author Affiliations
Joseph Y. Lo, Duke Univ. Medical Ctr. (United States)
Alan H. Baydush, Duke Univ. Medical Ctr. (United States)
Carey E. Floyd, Duke Univ. Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 2167:
Medical Imaging 1994: Image Processing
Murray H. Loew, Editor(s)

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