dorsal/arxiv
View SchemaMammographic image restoration using maximum entropy deconvolution
| Authors | A Jannetta, J C Jackson, C J Kotre, I P Birch, K J Robson, R Padgett |
|---|---|
| Categories | |
| ArXiv ID | physics/0505168 |
| URL | https://arxiv.org/abs/physics/0505168 |
| DOI | 10.1088/0031-9155/49/21/011 |
| Journal | Physics in Medicine & Biology, 49, 4997, 2004 |
Abstract
An image restoration approach based on a 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.
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"abstract": "An image restoration approach based on a Bayesian maximum entropy method\n(MEM) has been applied to a radiological image deconvolution problem, that of\nreduction of geometric blurring in magnification mammography. The aim of the\nwork is to demonstrate an improvement in image spatial resolution in realistic\nnoisy radiological images with no associated penalty in terms of reduction in\nthe signal-to-noise ratio perceived by the observer. Images of the TORMAM\nmammographic image quality phantom were recorded using the standard\nmagnification settings of 1.8 magnification/fine focus and also at 1.8\nmagnification/broad focus and 3.0 magnification/fine focus; the latter two\narrangements would normally give rise to unacceptable geometric blurring.\nMeasured point-spread functions were used in conjunction with the MEM image\nprocessing to de-blur these images. The results are presented as comparative\nimages of phantom test features and as observer scores for the raw and\nprocessed images. Visualization of high resolution features and the total image\nscores for the test phantom were improved by the application of the MEM\nprocessing. It is argued that this successful demonstration of image\nde-blurring in noisy radiological images offers the possibility of weakening\nthe link between focal spot size and geometric blurring in radiology, thus\nopening up new approaches to system optimization.",
"arxiv_id": "physics/0505168",
"authors": [
"A Jannetta",
"J C Jackson",
"C J Kotre",
"I P Birch",
"K J Robson",
"R Padgett"
],
"categories": [
"physics.med-ph",
"physics.data-an",
"physics.ins-det"
],
"doi": "10.1088/0031-9155/49/21/011",
"journal_ref": "Physics in Medicine \u0026 Biology, 49, 4997, 2004",
"title": "Mammographic image restoration using maximum entropy deconvolution",
"url": "https://arxiv.org/abs/physics/0505168"
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