A Single Sided Edge Marking Method for Detecting Pectoral Muscle in Digital Mammograms

Authors

  • G. Toz Electrical, Electronic & Computer Engineering Department, Duzce University, Turkey
  • P. Erdogmus Computer Engineering Department, Duzce University, Turkey
Volume: 8 | Issue: 1 | Pages: 2367-2373 | February 2018 | https://doi.org/10.48084/etasr.1719

Abstract

In the computer-assisted diagnosis of breast cancer, the removal of pectoral muscle from mammograms is very important. In this study, a new method, called Single-Sided Edge Marking (SSEM) technique, is proposed for the identification of the pectoral muscle border from mammograms. 60 mammograms from the INbreast database were used to test the proposed method. The results obtained were compared for False Positive Rate, False Negative Rate, and Sensitivity using the ground truth values pre-determined by radiologists for the same images. Accordingly, it has been shown that the proposed method can detect the pectoral muscle border with an average of 95.6% sensitivity.

Keywords:

INbreast database, pectoral muscle extraction, segmentation, mammogram

Downloads

Download data is not yet available.

References

S. Radhika, S. Sasikala, R. Mariappan, “Three-dimensional reconstruction of microcalcification clusters from CC and MLO views”, 2nd International Conference on Electronics and Communication Systems, pp. 607-611, 2015 DOI: https://doi.org/10.1109/ECS.2015.7124980

M. Tan, B. Zheng, “Development of a new case based computer-aided detection scheme for screening mammography”, IEEE EMBS Conference on Biomedical Engineering and Sciences, pp. 24-29, 2016 DOI: https://doi.org/10.1109/IECBES.2016.7843408

I. Christoyianni, A. Koutras, E. Dermatas, G. Kokkinakis, “Computer aided diagnosis of breast cancer in digitized mammograms”, Computerized Medical Imaging and Graphics, Vol. 26, No. 5, pp. 309-319, 2002 DOI: https://doi.org/10.1016/S0895-6111(02)00031-9

K. Doi, H. K. Huang, “Computer-aided diagnosis (CAD) and image-guided decision support”, Computerized Medical Imaging and Graphics, Vol. 31, No. 4-5, pp. 195-197, 2007 DOI: https://doi.org/10.1016/j.compmedimag.2007.02.001

R. J. Ferrari, R. M. Rangayyan, J. E. L. Desautels, R. A. Borges, A. F. Frere, “Automatic Identification of the Pectoral Muscle in Mammograms”, IEEE Transactions on Medical Imaging, Vol. 23, No. 2, pp. 232–245, 2004 DOI: https://doi.org/10.1109/TMI.2003.823062

A. Sultana, M. Ciuc, R. Strungaru, “Detection of pectoral muscle in mammograms using a mean-shift segmentation approach”, 8th International Conference on Communications, pp. 165–168, 2010 DOI: https://doi.org/10.1109/ICCOMM.2010.5509003

S. M. Kwok, R. Chandrasekhar, Y. Attikiouzel, “Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection”, The Seventh Australian and New Zealand Intelligent Information Systems Conference, pp. 67–72, 2001 DOI: https://doi.org/10.1109/ANZIIS.2001.974051

M. Yam, M. Brady, R. Highnam, C. Behrenbruch, R. English, Y.Kita, “Three-dimensional reconstruction of microcalcification clusters from two mammographic views”, IEEE Transactions on Medical Imaging, Vol. 20, No. 6, pp. 479–489, 2001 DOI: https://doi.org/10.1109/42.929614

X. Weidong, L. Lihua, L. A Wei, “Novel pectoral muscle segmentation algorithm based on polyline fitting and elastic thread approaching”, 1st International Conference on Bioinformatics and Biomedical Engineering, pp. 837–840, 2007

P. Miller, S. Astley, “Classification of breast tissue by texture analysis”, Image and Vision Computing, Vol. 10, No. 5, pp. 277–282, 1992 DOI: https://doi.org/10.1016/0262-8856(92)90042-2

A. Khademi, S. Farhang, A. Venetsanopoulos, K. Sridhar, “Region, Lesion and Border-Based Multiresolution Analysis of Mammogram Lesions Lecture Notes in Computer Science”, International Conference Image Analysis and Recognition, pp. 802–813, 2009 DOI: https://doi.org/10.1007/978-3-642-02611-9_79

K. Ganesan, U. Rajendra Acharya, K. Chua Chua, L. Choo Min, K. T. Abraham, “Pectoral muscle segmentation: A review”, Computer Methods and Programs in Biomedicine, Vol. 110, No. 1, pp. 48-57, 2013 DOI: https://doi.org/10.1016/j.cmpb.2012.10.020

M. Lee, Y. Chen, S. Kim, J. Kim, “Segmentation of the Pectoral Muscle Boundary in Breast MR Images”, IEEE 11th International Conference on Bioinformatics and Bioengineering, pp. 323-326, 2011 DOI: https://doi.org/10.1109/BIBE.2011.60

C. C. Liu, C. Y. Tsai, J. Liu, C.-Y. Yu, S. S. Yu, “A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis”, Computers & Mathematics with Applications, Vol. 64, No. 5, pp. 1100-1107, 2012 DOI: https://doi.org/10.1016/j.camwa.2012.03.028

S. S. Yu, C. Y. Tsai, C. C. Liu, “A breast region extraction scheme for digital mammograms using gradient vector flow Snake”, 4th International Conference on New Trends in Information Science and Service Science, pp. 715-720, 2010

S. Sreedevi, E. Sherly, “A Novel Approach for Removal of Pectoral Muscles in Digital Mammogram”, Procedia Computer Science, Vol. 46, pp. 1724-1731, 2015 DOI: https://doi.org/10.1016/j.procs.2015.02.117

I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso, J. S. Cardoso, “INbreast: Toward A Full-Field Digital Mammographic Database”, Academic Radiology, Vol. 19, No. 2, pp. 236-248, 2012 DOI: https://doi.org/10.1016/j.acra.2011.09.014

J. L. Starck, F. Murtagh, E. J. Candes, D. L. Donoho, “Gray and color image contrast enhancement by the curvelet transform”, IEEE Transactions on Image Processing, Vol. 12, No. 6, pp. 706-717, 2003 DOI: https://doi.org/10.1109/TIP.2003.813140

J. S. Lee, “Digital Image Enhancement and Noise Filtering by Use of Local Statistics”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 2, pp. 165-168, 1980 DOI: https://doi.org/10.1109/TPAMI.1980.4766994

E. S. Yelmanova, Y. M. Romanyshyn, “Medical image contrast enhancement based on histogram”, IEEE 37th International Conference on Electronics and Nanotechnology, pp. 273-278, 2017 DOI: https://doi.org/10.1109/ELNANO.2017.7939762

M. A. Abeed, A. K. Biswas, M. M. Al-Rashid, J. Atulasimha, S. Bandyopadhyay, “Image Processing With Dipole-Coupled Nanomagnets: Noise Suppression and Edge Enhancement Detection”, IEEE Transactions on Electron Devices, Vol. 64, No. 5, pp. 2417-2424, 2017 DOI: https://doi.org/10.1109/TED.2017.2679604

M. Regodic, L. Gigovic, Z. Bajic, S. Vasiljević “Contrast Enhancement of Colour Digital Images”, Tehnicki vjesnik, Vol. 24 No. 3, pp. 935-941, 2017 DOI: https://doi.org/10.17559/TV-20150410194409

S. H. Wang, T. M. Zhan, Y. Chen, Y. Zhang, M. Yang, H. M. Lu, H. N. Wang, B. Liu, P. Phillips, “Multiple Sclerosis Detection Based on Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression”, IEEE Access, Vol. 4, pp. 7567-7576, 2016 DOI: https://doi.org/10.1109/ACCESS.2016.2620996

M. Vetterli, J. Kovacevic, Wavelets and Subband Coding, Prentice Hall PTR, 1995

Downloads

How to Cite

[1]
G. Toz and P. Erdogmus, “A Single Sided Edge Marking Method for Detecting Pectoral Muscle in Digital Mammograms”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 1, pp. 2367–2373, Feb. 2018.

Metrics

Abstract Views: 733
PDF Downloads: 395

Metrics Information