Literature DB >> 9114122

Thickness-equalization processing for mammographic images.

J W Byng1, J P Critten, M J Yaffe.   

Abstract

A digital postprocessing technique was used to compensate for the limitations of laser film or cathode-ray-tube devices used to display digital mammograms. An algorithm identified and equalized for the large change in digital signal caused by the reduction in thickness at the margin of the compressed breast. The resulting images reflected only breast composition, and so the number of gray levels needed to display the processed image was greatly reduced, which facilitated presentation and analysis.

Mesh:

Year:  1997        PMID: 9114122     DOI: 10.1148/radiology.203.2.9114122

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  6 in total

1.  Model-based technique for the measurement of skin thickness in mammography.

Authors:  A Katartzis; H Sahli; J Cornelis; S Fotopoulos; G Panayiotakis
Journal:  Med Biol Eng Comput       Date:  2002-03       Impact factor: 2.602

2.  Identification of the breast boundary in mammograms using active contour models.

Authors:  R J Ferrari; R M Rangayyan; J E L Desautels; R A Borges; A F Frère
Journal:  Med Biol Eng Comput       Date:  2004-03       Impact factor: 2.602

3.  Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modelling.

Authors:  R J Ferrari; R M Rangayyan; R A Borges; A F Frère
Journal:  Med Biol Eng Comput       Date:  2004-05       Impact factor: 2.602

Review 4.  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

5.  Comparison of soft-copy and hard-copy reading for full-field digital mammography.

Authors:  Robert M Nishikawa; Suddhasatta Acharyya; Constantine Gatsonis; Etta D Pisano; Elodia B Cole; Helga S Marques; Carl J D'Orsi; Dione M Farria; Kalpana M Kanal; Mary C Mahoney; Murray Rebner; Melinda J Staiger
Journal:  Radiology       Date:  2009-04       Impact factor: 11.105

6.  Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain.

Authors:  Clíssia Barboza da Silva; Alysson Alexander Naves Silva; Geovanny Barroso; Pedro Takao Yamamoto; Valter Arthur; Claudio Fabiano Motta Toledo; Thiago de Araújo Mastrangelo
Journal:  Foods       Date:  2021-04-16
  6 in total

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