Literature DB >> 10434911

The effect of background structure on the detection of low contrast objects in mammography.

C J Kotre1.   

Abstract

The visual task of mammographic interpretation is considered in terms of the detection of a signal in the presence of noise, where the noise is taken to include a structure noise contribution from those areas of the imaged breast which do not contain the signal. The structure noise in a variety of typical mammographic parenchymal patterns was quantified and related to the area under observation using a statistical analysis of digitized image samples. Images of an anthropomorphic phantom were also analysed to establish it as a suitable test background for a series of contrast detail detection experiments. These experiments were performed with and without a structured background over a wide range of film dose, but at a fixed average film optical density. The presence of structure noise was found to reduce the detectability of low contrast objects, the effect becoming progressively smaller as the object size is reduced. Where the structured background was used, even large changes in dose to the film were found to produce little change in the overall contrast detail result except at the smallest detail diameters. These results are discussed in relation to existing theories of visual perception. It is suggested that the correlation between patient dose and cancer detection rate may be poorer than previously thought, as the detection task for objects larger than approximately 1 mm in diameter is dominated by the structure noise of the background parenchymal pattern rather than quantum noise.

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Year:  1998        PMID: 10434911     DOI: 10.1259/bjr.71.851.10434911

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  7 in total

1.  Visually lossless threshold determination for microcalcification detection in wavelet compressed mammograms.

Authors:  O Kocsis; L Costaridou; L Varaki; E Likaki; C Kalogeropoulou; S Skiadopoulos; G Panayiotakis
Journal:  Eur Radiol       Date:  2003-02-15       Impact factor: 5.315

2.  Defective pixels in medical LCD displays: problem analysis and fundamental solution.

Authors:  Tom Kimpe
Journal:  J Digit Imaging       Date:  2006-03       Impact factor: 4.056

Review 3.  Digital mammography: what do we and what don't we know?

Authors:  Ulrich Bick; Felix Diekmann
Journal:  Eur Radiol       Date:  2007-02-14       Impact factor: 5.315

4.  Receiver operating characteristic analysis for the detection of simulated microcalcifications on mammograms using hardcopy images.

Authors:  Chao-Jen Lai; Chris C Shaw; Gary J Whitman; Wei T Yang; Peter J Dempsey; Victoria Nguyen; Mary F Ice
Journal:  Phys Med Biol       Date:  2006-07-26       Impact factor: 3.609

5.  Comparison of deep learning and human observer performance for detection and characterization of simulated lesions.

Authors:  Ruben De Man; Grace J Gang; Xin Li; Ge Wang
Journal:  J Med Imaging (Bellingham)       Date:  2019-06-21

6.  The relationship between cancer detection in mammography and image quality measurements.

Authors:  Alistair Mackenzie; Lucy M Warren; Matthew G Wallis; Rosalind M Given-Wilson; Julie Cooke; David R Dance; Dev P Chakraborty; Mark D Halling-Brown; Padraig T Looney; Kenneth C Young
Journal:  Phys Med       Date:  2016-04-06       Impact factor: 2.685

7.  Comparison of low-contrast detectability between uniform and anatomically realistic phantoms-influences on CT image quality assessment.

Authors:  Juliane Conzelmann; Ulrich Genske; Arthur Emig; Michael Scheel; Bernd Hamm; Paul Jahnke
Journal:  Eur Radiol       Date:  2021-09-02       Impact factor: 5.315

  7 in total

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