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Proceedings Paper

Image deconvolution as an aid to mammographic artifact identification: I. Basic techniques
Author(s): Phillip Abbott; Andrew Shearer; Triona O'Doherty; Wil van der Putten
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Paper Abstract

Digital mammography has the potential to provide radiologists with a tool which can detect tumors earlier and with greater accuracy then film based systems. Although a digital mammography system can provide much greater contrast when compared with a conventional film system, the ability to detect small artifacts associated with breast cancer is limited by a reduced spatial resolution due to screen unsharpness and scatter induced fog. In this paper we model the radiological image formation process as the convolution of a linear shift invariant point spread function (PSF) with the projected tissue density source function. We model the PSF as consisting of two components--screen unsharpness and scatter. We present results from a method designed to compensate for screen unsharpness. The screen PSF was measured and subsequently used in an iterative deconvolution algorithm which incorporated wavelet based de-noising between steps in order to reduce noise amplification. When applied to a University of Leeds TORMAX breast phantom the results show as much as a two-fold improvement in resolution at the 50 percent MTF level. Our results show that the regularized deconvolution algorithm significantly improves the signal-to-noise ratio in the restored image.

Paper Details

Date Published: 21 May 1999
PDF: 12 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348627
Show Author Affiliations
Phillip Abbott, National Univ. of Ireland (Ireland)
Andrew Shearer, National Univ. of Ireland (Ireland)
Triona O'Doherty, National Univ. of Ireland (Ireland)
Wil van der Putten, Univ. College Hospital (Ireland)

Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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