Literature DB >> 15305451

On the noise variance of a digital mammography system.

Arthur Burgess1.   

Abstract

A recent paper by Cooper et al. [Med. Phys. 30, 2614-2621 (2003)] contains some apparently anomalous results concerning the relationship between pixel variance and x-ray exposure for a digital mammography system. They found an unexpected peak in a display domain pixel variance plot as a function of 1/mAs (their Fig. 5) with a decrease in the range corresponding to high display data values, corresponding to low x-ray exposures. As they pointed out, if the detector response is linear in exposure and the transformation from raw to display data scales is logarithmic, then pixel variance should be a monotonically increasing function in the figure. They concluded that the total system transfer curve, between input exposure and display image data values, is not logarithmic over the full exposure range. They separated data analysis into two regions and plotted the logarithm of display image pixel variance as a function of the logarithm of the mAs used to produce the phantom images. They found a slope of minus one for high mAs values and concluded that the transfer function is logarithmic in this region. They found a slope of 0.6 for the low mAs region and concluded that the transfer curve was neither linear nor logarithmic for low exposure values. It is known that the digital mammography system investigated by Cooper et al. has a linear relationship between exposure and raw data values [Vedantham et al., Med. Phys. 27, 558-567 (2000)]. The purpose of this paper is to show that the variance effect found by Cooper et al. (their Fig. 5) arises because the transformation from the raw data scale (14 bits) to the display scale (12 bits), for the digital mammography system they investigated, is not logarithmic for raw data values less than about 300 (display data values greater than about 3300). At low raw data values the transformation is linear and prevents over-ranging of the display data scale. Parametric models for the two transformations will be presented. Results of pixel variance measurements made on raw data images will be presented. The experimental data are in good agreement with those of Cooper et al. It will be shown that the slope of 0.6 found by Cooper et al. for the log-log plot at low exposure is not due to transfer function nonlinearity, it occurs because of an additive variance term-possibly due to electronic noise. It will also be shown, using population statistics from clinical images, that raw data values below 300 are rare in tissue areas. Those tissue areas with very low raw data values are within about a millimeter of the chest wall or in very dense muscle at comers of images.

Mesh:

Year:  2004        PMID: 15305451     DOI: 10.1118/1.1758791

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  14 in total

1.  Effects of exposure equalization on image signal-to-noise ratios in digital mammography: a simulation study with an anthropomorphic breast phantom.

Authors:  Xinming Liu; Chao-Jen Lai; Gary J Whitman; William R Geiser; Youtao Shen; Ying Yi; Chris C Shaw
Journal:  Med Phys       Date:  2011-12       Impact factor: 4.071

2.  Noise variance analysis using a flat panel x-ray detector: a method for additive noise assessment with application to breast CT applications.

Authors:  Kai Yang; Shih-Ying Huang; Nathan J Packard; John M Boone
Journal:  Med Phys       Date:  2010-07       Impact factor: 4.071

3.  Computerized image analysis: texture-field orientation method for pectoral muscle identification on MLO-view mammograms.

Authors:  Chuan Zhou; Jun Wei; Heang-Ping Chan; Chintana Paramagul; Lubomir M Hadjiiski; Berkman Sahiner; Julie A Douglas
Journal:  Med Phys       Date:  2010-05       Impact factor: 4.071

4.  Computer-aided detection of breast masses on full field digital mammograms.

Authors:  Jun Wei; Berkman Sahiner; Lubomir M Hadjiiski; Heang-Ping Chan; Nicholas Petrick; Mark A Helvie; Marilyn A Roubidoux; Jun Ge; Chuan Zhou
Journal:  Med Phys       Date:  2005-09       Impact factor: 4.071

5.  Computer-aided detection system for clustered microcalcifications: comparison of performance on full-field digital mammograms and digitized screen-film mammograms.

Authors:  Jun Ge; Lubomir M Hadjiiski; Berkman Sahiner; Jun Wei; Mark A Helvie; Chuan Zhou; Heang-Ping Chan
Journal:  Phys Med Biol       Date:  2007-01-23       Impact factor: 3.609

6.  Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.

Authors:  Ravi K Samala; Heang-Ping Chan; Lubomir Hadjiiski; Mark A Helvie; Jun Wei; Kenny Cha
Journal:  Med Phys       Date:  2016-12       Impact factor: 4.071

7.  Investigation of noise sources for digital radiography systems.

Authors:  Lutfi Ergun; Turan Olgar
Journal:  Radiol Phys Technol       Date:  2016-10-01

8.  Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.

Authors:  Ravi K Samala; Heang-Ping Chan; Lubomir M Hadjiiski; Mark A Helvie; Kenny H Cha; Caleb D Richter
Journal:  Phys Med Biol       Date:  2017-11-10       Impact factor: 3.609

9.  An alternate method for using a visual discrimination model (VDM) to optimize soft-copy display image quality.

Authors:  Dev P Chakraborty
Journal:  J Soc Inf Disp       Date:  2006-10       Impact factor: 2.140

10.  An X-Ray computed tomography/positron emission tomography system designed specifically for breast imaging.

Authors:  John M Boone; Kai Yang; George W Burkett; Nathan J Packard; Shih-ying Huang; Spencer Bowen; Ramsey D Badawi; Karen K Lindfors
Journal:  Technol Cancer Res Treat       Date:  2010-02
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