Literature DB >> 15125150

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

R J Ferrari1, R M Rangayyan, J E L Desautels, R A Borges, A F Frère.   

Abstract

A method for the identification of the breast boundary in mammograms is presented. The method can be used in the preprocessing stage of a system for computer-aided diagnosis (CAD) of breast cancer and also in the reduction of image file size in picture archiving and communication system applications. The method started with modification of the contrast of the original image. A binarisation procedure was then applied to the image, and the chain-code algorithm was used to find an approximate breast contour. Finally, the identification of the true breast boundary was performed by using the approximate contour as the input to an active contour model algorithm specially tailored for this purpose. After demarcation of the breast boundary, all artifacts outside the breast region were eliminated. The method was applied to 84 medio-lateral oblique mammograms from the Mini-MIAS database. Evaluation of the detected breast boundary was performed based upon the percentage of false-positive and false-negative pixels determined by a quantitative comparison between the contours identified by a radiologist and those identified by the proposed method. The average false positive and false negative rates were 0.41% and 0.58%, respectively. The two radiologists who evaluated the results considered the segmentation results to be acceptable for CAD purposes.

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Year:  2004        PMID: 15125150     DOI: 10.1007/bf02344632

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  9 in total

1.  Automatic breast region extraction from digital mammograms for PACS and telemammography applications.

Authors:  S L Lou; H D Lin; K P Lin; D Hoogstrate
Journal:  Comput Med Imaging Graph       Date:  2000 Jul-Aug       Impact factor: 4.790

2.  Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets.

Authors:  R J Ferrari; R M Rangayyan; J E Desautels; A F Frère
Journal:  IEEE Trans Med Imaging       Date:  2001-09       Impact factor: 10.048

3.  A discrete dynamic contour model.

Authors:  S Lobregt; M A Viergever
Journal:  IEEE Trans Med Imaging       Date:  1995       Impact factor: 10.048

4.  Automated detection of breast tumors using the asymmetry approach.

Authors:  T K Lau; W F Bischof
Journal:  Comput Biomed Res       Date:  1991-06

5.  Automated segmentation of digitized mammograms.

Authors:  U Bick; M L Giger; R A Schmidt; R M Nishikawa; D E Wolverton; K Doi
Journal:  Acad Radiol       Date:  1995-01       Impact factor: 3.173

6.  Automatic detection of breast border and nipple in digital mammograms.

Authors:  A J Méndez; P G Tahoces; M J Lado; M Souto; J L Correa; J J Vidal
Journal:  Comput Methods Programs Biomed       Date:  1996-05       Impact factor: 5.428

7.  Density correction of peripheral breast tissue on digital mammograms.

Authors:  U Bick; M L Giger; R A Schmidt; R M Nishikawa; K Doi
Journal:  Radiographics       Date:  1996-11       Impact factor: 5.333

8.  Thickness-equalization processing for mammographic images.

Authors:  J W Byng; J P Critten; M J Yaffe
Journal:  Radiology       Date:  1997-05       Impact factor: 11.105

9.  A simple method for automatically locating the nipple on mammograms.

Authors:  R Chandrasekhar; Y Attikiouzel
Journal:  IEEE Trans Med Imaging       Date:  1997-10       Impact factor: 10.048

  9 in total
  17 in total

1.  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 2.  Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms.

Authors:  Mario Mustra; Mislav Grgic; Rangaraj M Rangayyan
Journal:  Med Biol Eng Comput       Date:  2015-11-06       Impact factor: 2.602

3.  Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models.

Authors:  Wen-Li Lee; Koyin Chang; Kai-Sheng Hsieh
Journal:  Med Biol Eng Comput       Date:  2015-11-03       Impact factor: 2.602

4.  Gabor filters and phase portraits for the detection of architectural distortion in mammograms.

Authors:  Rangaraj M Rangayyan; Fábio J Ayres
Journal:  Med Biol Eng Comput       Date:  2006-08-11       Impact factor: 2.602

5.  An automatic correction method for the heel effect in digitized mammography images.

Authors:  Marcelo Zanchetta do Nascimento; Annie France Frère; Fernao Germano
Journal:  J Digit Imaging       Date:  2007-09-11       Impact factor: 4.056

6.  Dynamic multiple thresholding breast boundary detection algorithm for mammograms.

Authors:  Yi-Ta Wu; Chuan Zhou; Heang-Ping Chan; Chintana Paramagul; Lubomir M Hadjiiski; Caroline Plowden Daly; Julie A Douglas; Yiheng Zhang; Berkman Sahiner; Jiazheng Shi; Jun Wei
Journal:  Med Phys       Date:  2010-01       Impact factor: 4.071

7.  Mammogram segmentation using maximal cell strength updation in cellular automata.

Authors:  J Anitha; J Dinesh Peter
Journal:  Med Biol Eng Comput       Date:  2015-04-05       Impact factor: 2.602

8.  Objective models of compressed breast shapes undergoing mammography.

Authors:  Steve Si Jia Feng; Bhavika Patel; Ioannis Sechopoulos
Journal:  Med Phys       Date:  2013-03       Impact factor: 4.071

9.  Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

Authors:  Shubhi Sharma; Pritee Khanna
Journal:  J Digit Imaging       Date:  2014-07-09       Impact factor: 4.056

10.  An automated approach for estimation of breast density.

Authors:  John J Heine; Michael J Carston; Christopher G Scott; Kathleen R Brandt; Fang-Fang Wu; Vernon Shane Pankratz; Thomas A Sellers; Celine M Vachon
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-11       Impact factor: 4.254

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