Image deconvolution as an aid to mammographic artifact identification: I. Basic techniques
Phillip Abbott, A. Shearer, Triona O'Doherty, Wil van der Putten · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
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.