Literature DB >> 11120407

Accurate segmentation of the breast region from digitized mammograms.

T Ojala1, J Näppi, O Nevalainen.   

Abstract

The segmentation of a digital mammogram into the breast region and the background is a necessary prerequisite in computer-assisted diagnosis of mammograms. By the exclusion of the background region, the accuracy of the analysis is increased and the running-time is decreased. The algorithm which segments the breast region from the background should be fully automated and give correct results for all kinds of digitized mammograms, including low-quality images. In this paper we present such an algorithm based on histogram thresholding, morphological filtering and contour modeling. Quantitative test results indicate that the computed boundary follows the estimated boundary accurately.

Mesh:

Year:  2001        PMID: 11120407     DOI: 10.1016/s0895-6111(00)00036-7

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  5 in total

1.  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

2.  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

Review 3.  Breast image registration techniques: a survey.

Authors:  Yujun Guo; Radhika Sivaramakrishna; Cheng-Chang Lu; Jasjit S Suri; Swamy Laxminarayan
Journal:  Med Biol Eng Comput       Date:  2006-03       Impact factor: 2.602

4.  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

Review 5.  Breast Cancer Segmentation Methods: Current Status and Future Potentials.

Authors:  Epimack Michael; He Ma; Hong Li; Frank Kulwa; Jing Li
Journal:  Biomed Res Int       Date:  2021-07-20       Impact factor: 3.411

  5 in total

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