Literature DB >> 16820957

Contrast enhancement in dense breast images to aid clustered microcalcifications detection.

Fátima L S Nunes1, Homero Schiabel, Claudio E Goes.   

Abstract

This paper presents a method to provide contrast enhancement in dense breast digitized images, which are difficult cases in testing of computer-aided diagnosis (CAD) schemes. Three techniques were developed, and data from each method were combined to provide a better result in relation to detection of clustered microcalcifications. Results obtained during the tests indicated that, by combining all the developed techniques, it is possible to improve the performance of a processing scheme designed to detect microcalcification clusters. It also allows operators to distinguish some of these structures in low-contrast images, which were not detected via conventional processing before the contrast enhancement. This investigation shows the possibility of improving CAD schemes for better detection of microcalcifications in dense breast images.

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Year:  2007        PMID: 16820957      PMCID: PMC3043882          DOI: 10.1007/s10278-005-6976-5

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  9 in total

1.  Statistical textural features for detection of microcalcifications in digitized mammograms.

Authors:  J K Kim; H W Park
Journal:  IEEE Trans Med Imaging       Date:  1999-03       Impact factor: 10.048

2.  An automatic method for the identification and interpretation of clustered microcalcifications in mammograms.

Authors:  F Schmidt; E Sorantin; C Szepesvàri; E Graif; M Becker; H Mayer; K Hartwagner
Journal:  Phys Med Biol       Date:  1999-05       Impact factor: 3.609

3.  Scale-space signatures for the detection of clustered microcalculations in digital mammograms.

Authors:  T Netsch; H O Peitgen
Journal:  IEEE Trans Med Imaging       Date:  1999-09       Impact factor: 10.048

4.  Segmentation of suspicious clustered microcalcifications in mammograms.

Authors:  M A Gavrielides; J Y Lo; R Vargas-Voracek; C E Floyd
Journal:  Med Phys       Date:  2000-01       Impact factor: 4.071

5.  A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films.

Authors:  S Yu; L Guan
Journal:  IEEE Trans Med Imaging       Date:  2000-02       Impact factor: 10.048

Review 6.  Image processing and computer-aided diagnosis.

Authors:  M Giger; H MacMahon
Journal:  Radiol Clin North Am       Date:  1996-05       Impact factor: 2.303

7.  Computer-aided detection of clustered microcalcifications: an improved method for grouping detected signals.

Authors:  R M Nishikawa; M L Giger; K Doi; C J Vyborny; R A Schmidt
Journal:  Med Phys       Date:  1993 Nov-Dec       Impact factor: 4.071

8.  [Diagnostic value of clustered microcalcifications discovered by mammography (apropos of 227 cases with histological verification and without a palpable breast tumor)].

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Journal:  Bull Cancer       Date:  1984       Impact factor: 1.276

9.  Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study.

Authors:  N F Boyd; J W Byng; R A Jong; E K Fishell; L E Little; A B Miller; G A Lockwood; D L Tritchler; M J Yaffe
Journal:  J Natl Cancer Inst       Date:  1995-05-03       Impact factor: 13.506

  9 in total
  4 in total

1.  A new fast fractal modeling approach for the detection of microcalcifications in mammograms.

Authors:  Deepa Sankar; Tessamma Thomas
Journal:  J Digit Imaging       Date:  2009-07-18       Impact factor: 4.056

2.  Mammographic image denoising and enhancement using the Anscombe transformation, adaptive wiener filtering, and the modulation transfer function.

Authors:  Larissa C S Romualdo; Marcelo A C Vieira; Homero Schiabel; Nelson D A Mascarenhas; Lucas R Borges
Journal:  J Digit Imaging       Date:  2013-04       Impact factor: 4.056

Review 3.  Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review.

Authors:  Saleem Z Ramadan
Journal:  J Healthc Eng       Date:  2020-03-12       Impact factor: 2.682

Review 4.  Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.

Authors:  Afsaneh Jalalian; Syamsiah Mashohor; Rozi Mahmud; Babak Karasfi; M Iqbal B Saripan; Abdul Rahman B Ramli
Journal:  EXCLI J       Date:  2017-02-20       Impact factor: 4.068

  4 in total

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