Literature DB >> 11249089

Automatic segmentation of mammographic density.

R Sivaramakrishna1, N A Obuchowski, W A Chilcote, K A Powell.   

Abstract

RATIONALE AND
OBJECTIVES: The purpose of this study was to evaluate a completely automatic method, based on Kittler's optimal threshold, to estimate breast density by using the mammographers' definition.
MATERIALS AND METHODS: Thirty-two normal, right-craniocaudal-view mammograms of women aged 37-86 years were digitized. The whole breast area was segmented by using Kittler's optimal threshold procedure, and the dense portions were then segmented by using a modified version of Kittler's method. Segmentation results were validated by three independent mammographers who provided a signed percentage (in steps of 5%) to indicate the difference between their own visual estimation of the dense portions and the results obtained with the algorithm. The difference between the algorithm measurements and the mammographers' measurements was compared to the interobserver differences.
RESULTS: A high correlation was found between the algorithm measured density and the mammographers' measurements. Spearman correlations ranged from 0.92 to 0.95 (P < .001). Algorithm-measured density differed from the mammographers' measurements by an average of 6.9% (ie, average of the absolute differences). In contrast, mammographers' measurements differed between themselves by an average of 5.4%.
CONCLUSION: The difference between density as measured with the algorithm and as measured by the mammographers is similar to the differences observed between mammographers. This algorithm could be useful in providing clinically accurate estimates of breast density.

Entities:  

Mesh:

Year:  2001        PMID: 11249089     DOI: 10.1016/S1076-6332(03)80534-2

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  13 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

2.  Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study.

Authors:  Huanjun Ding; Sabee Molloi
Journal:  Phys Med Biol       Date:  2012-07-06       Impact factor: 3.609

3.  Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit.

Authors:  Lee-Jane W Lu; Thomas K Nishino; Tuenchit Khamapirad; James J Grady; Morton H Leonard; Donald G Brunder
Journal:  Phys Med Biol       Date:  2007-07-30       Impact factor: 3.609

4.  A statistical approach for breast density segmentation.

Authors:  Arnau Oliver; Xavier Lladó; Elsa Pérez; Josep Pont; Erika R E Denton; Jordi Freixenet; Joan Martí
Journal:  J Digit Imaging       Date:  2009-06-09       Impact factor: 4.056

5.  Classification of breast computed tomography data.

Authors:  Thomas R Nelson; Laura I Cerviño; John M Boone; Karen K Lindfors
Journal:  Med Phys       Date:  2008-03       Impact factor: 4.071

6.  Postmortem validation of breast density using dual-energy mammography.

Authors:  Sabee Molloi; Justin L Ducote; Huanjun Ding; Stephen A Feig
Journal:  Med Phys       Date:  2014-08       Impact factor: 4.071

7.  Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm.

Authors:  Lee-Jane W Lu; Thomas K Nishino; Raleigh F Johnson; Fatima Nayeem; Donald G Brunder; Hyunsu Ju; Morton H Leonard; James J Grady; Tuenchit Khamapirad
Journal:  Phys Med Biol       Date:  2012-10-09       Impact factor: 3.609

8.  Feasibility study of a breast density measurement within a direct photon-counting mammography scanner system.

Authors:  Youichi Machida; Mitsuhiro Tozaki; Tamiko Yoshida; Ai Saita; Mari Yakabe; Kanae Nii
Journal:  Jpn J Radiol       Date:  2014-05-18       Impact factor: 2.374

Review 9.  A Review on Automatic Mammographic Density and Parenchymal Segmentation.

Authors:  Wenda He; Arne Juette; Erika R E Denton; Arnau Oliver; Robert Martí; Reyer Zwiggelaar
Journal:  Int J Breast Cancer       Date:  2015-06-11

Review 10.  Mammographic density. Measurement of mammographic density.

Authors:  Martin J Yaffe
Journal:  Breast Cancer Res       Date:  2008-06-19       Impact factor: 6.466

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