Mammographic image restoration using maximum entropy deconvolution

Adrian Jannetta, John Jackson, Ian Birch, John Kotre, Kevin Robson, Rod Padgett

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Bayesian maximum entropy method (MEM) has been applied to a radiological image deconvolution problem, that of reduction of geometric blurring in magnification mammography. The aim of the work is to demonstrate an improvement in image spatial resolution in realistic noisy radiological images with no associated penalty in terms of reduction in the signal-to-noise ratio perceived by the observer. Images of the TORMAM mammographic image quality phantom were recorded using the standard magnification settings of 1.8 magnification/fine focus and also at 1.8 magnification/broad focus and 3.0 magnification/fine focus; the latter two arrangements would normally give rise to unacceptable geometric blurring. Measured point-spread functions were used in conjunction with the MEM image processing to de-blur these images. The results are presented as comparative images of phantom test features and as observer scores for the raw and processed images. Visualization of high resolution features and the total image scores for the test phantom were improved by the application of the MEM processing. It is argued that this successful demonstration of image de-blurring in noisy radiological images offers the possibility of weakening the link between focal spot size and geometric blurring in radiology, thus opening up new approaches to system optimization.
Original languageEnglish
Pages (from-to)4997-5010
JournalPhysics in Medicine and Biology
Volume49
Issue number21
DOIs
Publication statusPublished - 7 Nov 2004

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