Literature DB >> 23270962

Pectoral muscle segmentation: a review.

Karthikeyan Ganesan1, U Rajendra Acharya, Kuang Chua Chua, Lim Choo Min, K Thomas Abraham.   

Abstract

Mammograms are X-ray images of breasts which are used to detect breast cancer. The pectoral muscle is a mass of tissue on which the breast rests. During routine mammographic screenings, in medio-lateral oblique (MLO) views, the pectoral muscle turns up in the mammograms along with the breast tissues. The pectoral muscle has to be segmented from the mammogram for an effective automated computer aided diagnosis (CAD). This is due to the fact that pectoral muscles have pixel intensities and texture similar to that of breast tissues which can result in awry CAD results. As a result, a lot of effort has been put into the segmentation of pectoral muscles and finding its contour with the breast tissues. To the best of our knowledge, currently there is no definitive literature available which provides a comprehensive review about the current state of research in this area of pectoral muscle segmentation. We try to address this shortcoming by providing a comprehensive review of research papers in this area. A conscious effort has been made to avoid deviating into the area of automated breast cancer detection.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Year:  2012        PMID: 23270962     DOI: 10.1016/j.cmpb.2012.10.020

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  12 in total

1.  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

2.  Pectoral muscle detection in mammograms using local statistical features.

Authors:  Li Liu; Qian Liu; Wei Lu
Journal:  J Digit Imaging       Date:  2014-10       Impact factor: 4.056

3.  Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms.

Authors:  Chia-Hung Wei; Chih-Ying Gwo; Pai Jung Huang
Journal:  Br J Radiol       Date:  2016-04-04       Impact factor: 3.039

4.  Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter.

Authors:  P S Vikhe; V R Thool
Journal:  J Med Syst       Date:  2017-10-25       Impact factor: 4.460

5.  Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.

Authors:  Xiangyuan Ma; Jun Wei; Chuan Zhou; Mark A Helvie; Heang-Ping Chan; Lubomir M Hadjiiski; Yao Lu
Journal:  Med Phys       Date:  2019-03-12       Impact factor: 4.071

6.  Automated breast segmentation of fat and water MR images using dynamic programming.

Authors:  José A Rosado-Toro; Tomoe Barr; Jean-Philippe Galons; Marilyn T Marron; Alison Stopeck; Cynthia Thomson; Patricia Thompson; Danielle Carroll; Eszter Wolf; María I Altbach; Jeffrey J Rodríguez
Journal:  Acad Radiol       Date:  2015-02       Impact factor: 3.173

7.  Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs.

Authors:  Dinesh Pandey; Xiaoxia Yin; Hua Wang; Min-Ying Su; Jeon-Hor Chen; Jianlin Wu; Yanchun Zhang
Journal:  Heliyon       Date:  2018-12-17

8.  Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms.

Authors:  José Celaya-Padilla; Antonio Martinez-Torteya; Juan Rodriguez-Rojas; Jorge Galvan-Tejada; Victor Treviño; José Tamez-Peña
Journal:  Biomed Res Int       Date:  2015-07-09       Impact factor: 3.411

9.  Mammographic Breast Density in Chinese Women: Spatial Distribution and Autocorrelation Patterns.

Authors:  Christopher W K Lai; Helen K W Law
Journal:  PLoS One       Date:  2015-09-02       Impact factor: 3.240

10.  Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms.

Authors:  Meenakshi M Pawar; Sanjay N Talbar; Akshay Dudhane
Journal:  J Healthc Eng       Date:  2018-09-25       Impact factor: 2.682

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