Neural network scatter correction technique for digital radiography

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


A scatter correction technique based on artificial neural networks is presented. The technique utilizes the acquisition of a conventional digital radiographic image, couple with the acquisition of a multiple pencil beam ('micro-aperture') digital image. Image subtraction results in a sparsed sampled estimate of the scatter component in the image. The neural network is trained to develop a causal relationship between image data on the low-pass filtered open field image and the sparsely sampled scatter image, and then the trained network is used to correct the entire image (pixel by pixel) in a manner which is operationally similar to but potentially more powerful than convolution. The technique is described and is illustrated using clinical 'primary' component images combined with scatter component images that are realistically simulated using the results from previously reported Monte Carlo investigations. The results indicate that an accurate scatter correction can be realized using this technique.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsH.Roger Schneider
Place of PublicationBellingham, WA, United States
PublisherPubl by Int Soc for Optical Engineering
Number of pages10
ISBN (Print)0819402753
StatePublished - 1990
EventMedical Imaging IV: Image Foundation - Newport Beach, CA, USA
Duration: Feb 4 1990Feb 6 1990


OtherMedical Imaging IV: Image Foundation
CityNewport Beach, CA, USA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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