Abstract
Substantial numbers of patients are reaching to a progressive breast cancer stage due to increase in the false negatives coming out of cumbersome and tedious job of continuously observing the mammograms in fatigue. Hence, the early detection of cancer with more accuracy is highly expected to reduce the death rate. Computer Aided Detection (CADe) can help radiologists in providing a second opinion increasing the overall accuracy of detection. Pectoral muscle is a predominant density area in most mammograms and may bias the results. Its extraction can increase accuracy and efficiency of cancer detection. This work is intended to provide the researchers a systematic and comprehensive overview of different techniques of pectoral muscle extraction which are categorized into groups based on intensity, region, gradient, transform, probability and polynomial, active contour, graph theory, and soft computing approaches. The performance of all these methods is summarized in tabular form for comparison purpose. The accuracy, efficiency and computational complexities of some selected methods are discussed in view of deciding a best approach in each of the categories.
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The American College of Radiology.: http://www.mammographysaveslives.org/Facts
Amin, I.I., Hassanien, A.E., Kassim, S.K., Hefny, H.A.: Big DNA Methylation data analysis and visualizing in a common form of breast cancer. In: Hassanien, A.E. et al. (eds.) Big Data in Complex Systems, Studies in Big Data, vol. 9. Springer International Publishing Switzerland (2015). doi:10.1007/978-3-319-11056-1_13
Hassanien, A.E., Moftah, H.M., Azar, A.T., Shoman, M.: MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Appl. Soft Comput. 14, 62–71 (2014)
Moftah, H.M., Azar, A.T., Al-Shammari, E.T., Ghali, N.I., Hassanien, A.E., Shoman, M.: Adaptive K-means clustering algorithm for MR breast image segmentation. Neural Comput. Appl. 24(7–8), 1917–1928 (2014)
American Cancer Society.: http://www.cancer.org/treatment/understandingyourdiagnosis
BreastCancer.Org.: http://www.breastcancer.org/symptoms/testing/types/mammograms
Radiopaedia.org.: http://radiopaedia.org/articles/mediolateral-oblique-view
Bhateja, V., Misra, M., Urooj, S., Lay-Ekuakille, A.: A robust polynomial filtering framework for mammographic image enhancement from biomedical sensors. IEEE Sens. J. 13(11), 4147–4156 (2013)
Karthikeyan, G., Rajendra, A., Kuang, C.C., Lim, C.M., K Thomas, A.: Pectoral muscle segmentation: a review. Elsevier J. Comput. Methods Progr. Biomed. 48–57 (2013)
Suckling, J.: The Mammographic image analysis society digital mammogram database. In: Exerpta Medica. International Congress Series, vol. 1069, pp. 375–378 (1994)
Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: the digital database for screening mammography. In: Proceedings of the 5th International Workshop on Digital Mammography, pp. 212–218. Med. Physics Publishing (2001)
Thangavel, K., Karnan, M.: Computer aided diagnosis in digital mammograms: detection of micro-calcifications by meta heuristic algorithms. GVIP J. 5(7) (2005)
Camilus, K., Govindan, V., Sathidevi, P.: Pectoral muscle identification in mammograms. J. Appl. Clin. Med. Phys. North America 12(3), 215–230 (2011)
Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Kamila, C., Justyna, W.: Automatic breast-line and pectoral muscle segmentation. Schedae Inf. 20, 195–209 (2012)
Liu, C.C., Tsai, C.Y., Liu, J., Yub, C.Y., Yub, S.S.: A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis. Elsevier J. Comput. Math. Appl., 1100–1107 (2012)
Duarte, M.A., Alvarenga, A.V., Azevedo, C.M., Infantosi, A.F.C., Pereira, W.C.A.: Estimating the pectoral muscle and the nipple positions in mammographies using morphological filters. In: XXIII Congresso Brasileiro em Engenharia Biomédica (2012)
Burcin, K., Nabiyevb, V.V., Turhanc, K.: A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu’s N thresholding. Comput. Methods Progr. Biomed., 349–360 (2014)
Raba, D., Oliver, A., Joan, M., Marta, P., Joan, E.: Breast segmentation with pectoral muscle suppression on digital mammograms. Lecture Notes in Computer Science, pp. 153–158 (2005)
Saltanat, N., Hossain, M.A., Alam, M.S.: An efficient pixel value based mapping scheme to delineate pectoral muscle from mammograms. In: IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), pp. 1510–1517 (2010)
Nagi, J., Kareem, S.A., Nagi, F., Ahmed, S.K.: Automated breast profile segmentation for ROI detection using digital mammograms. In: IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 87–92 (2010)
Nanayakkara, R.R., Yapa, Y.P.R.D., Hevawithana, P.B., Wijekoon, P.: Automatic breast boundary segmentation of mammograms. Int J. Soft Comput. Eng. (IJSCE). 5(1) (2015)
Bezdek, J.C., Chandrasekhar, R., Attikiouzel, Y.: A Geometric approach to edge detection. IEEE Trans. Fuzzy Syst. 6(1), 52–75 (1998)
Chandrasekhar, R., Attikiouzel, Y.: Segmentation of the pectoral muscle edge on mammograms by tunable parametric edge detection. In: Australian Research Centre for Medical Engineering (ARCME) (2000)
Ferrari, R.J., Rangayyan, R.M., Desautels, J.E.L., Frère, A.F.: Segmentation of mammograms: identification of the skin–air boundary, pectoral muscle, and fibro-glandular disc. In: Proceedings of 5th International Workshop on Digital Mammography, Toronto, ON, Canada. pp. 573–579 (2000)
Kwok, S.M., Chandrashekar, R. and Attikkiouzel, Y.: Automatic pectoral muscle segmentation on mammograms by straight line estimation and Cliff detection. In: Seventh Australian and New Zealand Intelligent Information Systems Conference, Perth, Western Australia, pp. 67–72 (2001)
Kwok, S.M., Chandrasekhar, R., Attikiouzel, Y., Rickard, M.T.: Automatic pectoral muscle segmentation on mediolateral oblique view mammograms. IEEE Trans. Med. Imaging 23(9), 1129–1139 (2004)
Kwok, S.M., Chandrasekhar, R.A., Attikiouzel, Y.: Automatic assessment of mammographic positioning on the mediolateral oblique view. In: International Conference on Image Processing ICIP ’04, vol. 1, pp. 151–154 (2004)
Weidong, X., Lihua, L., Wei, L.A.: Novel pectoral muscle segmentation algorithm based on polyline fitting and elastic thread approaching. In: The 1st International Conference on Bioinformatics and Biomedical Engineering, pp. 837–840 (2007)
Zhou, C., Wei, J., Chan, H.P., Paramagul, C., Hadjiiski, L.M., Sahiner, B.: Computerized image analysis: texture-field orientation method for pectoral muscle identification on MLO-view mammograms. Am. Assoc. Med. Phys., 2289–2299 (2010)
Chakraborty, J., Mukhopadhyay, S., Singla, V., Khandelwal, N., Bhattacharyya, P.: Automatic detection of pectoral muscle using average gradient and shape based feature. J. Digit. Imaging 25(3), 387–399 (2012)
Molinara, M., Marrocco, C., Tortorella, F.: Automatic Segmentation of the pectoral muscle in mediolateral oblique mammograms. In: IEEE Conference on Computer-Based Medical Systems, pp. 506–509 (2013)
Ferrari, R.J., Rangayyan, R.M., Desautels, J.E.L., Borges, R.A., Frere, A.F.: Automatic identification of the pectoral muscle in mammograms. IEEE Trans. Med. Imaging 23(2), 232–245 (2004)
Kinoshita, S.K., Azevedo-Marques, P.M., Pereira Jr, R.R., Rodrigues, J.A.H., Rangayyan, R.M.: Radon-domain detection of the nipple and the pectoral muscle in mammograms. J. Digit. Imaging 21(1), 37–49 (2008)
Mustra, M., Bozek, J., Grgic, M.: Breast border extraction and pectoral muscle detection using wavelet decomposition. In: Proceedings of IEEE Conference EUROCON’09, pp. 1426–1433 (2009)
Mencattini, A., Salmeri, M., Casti, P., Pepe, M.L.: Local active contour models and Gabor wavelets for an optimal breast region segmentation. Int. J. Comput. Assist. Radiol. Surg. (2012)
Li, Y., Chen, H., Yang, Y., Yang, N.: Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation. Elsevier J. Pattern Recogn. 46(3), 681–691 (2013)
Sultana, A., Ciuc, M., Strungaru, R.: Detection of pectoral muscle in mammograms using a mean-shift segmentation approach. In: IEEE 8th International Conference on Communications, pp. 165–168 (2010)
Liu, L., Wang, J., Wang, T.: Breast and pectoral muscle contours detection based on goodness of fit measure. In: IEEE 5th International Conference on Bioinformatics and Biomedical Engineering, pp. 1–4 (2011)
Mustra, M., Grgic, M.: Robust automatic breast and pectoral muscle segmentation from scanned mammograms. Elsevier J. Signal Process. 93(10), 2817–2827 (2013)
Oliver, A., Llado, X., Torrent, A., Marti, J.: One-shot segmentation of breast, pectoral muscle, and background in digitized mammograms. In: IEEE International Conference on Image Processing (ICIP), pp. 912–916 (2014)
Wirth, M.A., Stapinski, A.: Segmentation of the breast region in mammograms using active contours. In: Visual Communications and Image Processing, International Society for Optics and Photonics, pp. 1995–2006 (2003)
Ferrari, R.J., Frere, A.F., Rangayyan, R.M., Desautels, J.E.L., Borges, R.A.: Identification of the breast boundary in mammograms using active contour models. J. Med. Biol. Eng. Comput. 42(2), 201–208 (2004)
Chaabani, A.C., Boujelben, A., Mahfoudhi, A., Abid, M.: An automatic-pre-processing method for mammographic images. Int. J. Digit. Content Technol. Appl. 4(3) (2010)
Wang, L., Zhu, M., Deng, L., Yuan, X.: Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model. J. Zhejiang Univ.-Sci. C (Computers & Electronics) 11(2), 111–118 (2010). ISSN 1869-1951
Kim, J.H., Park, B.Y., Akram, F., Hong, B.W., Choi, K.N.: Multipass active contours for an adaptive contour map. Sensors 13(3), 3724–3738 (2013). ISSN 1424-8220
Akram, F., Kim, J.H., Whoang, I., Choi, K.N.: A preprocessing algorithm for the CAD system of mammograms using the active contour method. Appl. Med. Inf. 32(2), 1–13 (2013)
Ma, F., Bajgera, M., John, P., Slavotinekb, Bottemaa, M.J.: Two graph theory based methods for identifying the pectoral muscle in mammograms. J. Pattern Recogn. 40, 2592–2602 (2007)
Camilus, K.S., Govindan, V.K., Sathidevi, P.S.: Computer-aided identification of the pectoral muscle in digitized mammograms. J. Digit. Imaging 23(5), 562–580 (2010)
Cardoso, J.S., Domingues, I., Amaral, I., Moreira, I., Passarinho, P., Comba, J.S., Correia, R., and Cardoso, M.J.: Pectoral muscle detection in mammograms based on polar coordinates and the shortest path. In: Engineering in Medicine and Biology Society, Annual International Conference of the IEEE., pp. 4781–4784 (2010)
Karnan, M., Thangavel, K.: Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of micro-calcifications. Comput. Methods Programs Biomed. 87(1), 12–20 (2007)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Domingues, I., Cardoso, J.S., Amaral, I., Moreira, I., Passarinho, P., Comba, J.S., Correia, R., Cardoso, M.J.: Pectoral muscle detection in mammograms based on the shortest path with endpoints learnt by SVMs. In: 32nd Annual Internatioanal Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3158–3161 (2010)
Aroquiaraj, I.L., Thangavel, K.: Pectoral Muscles Suppression in Digital Mammograms using Hybridization of Soft Computing Methods (2014). arXiv:1401.0870
Sapate, S.G., Talbar, S.N.: Pectoral muscle extraction using modified K-means algorithm for digital mammograms. J. Med. Phys. (2016)
Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. J. R. Stat. Soc. Blackwell Publishing 28(1), 100–108 (1979)
Acknowledgments
The authors are thankful to Dr. L.M. Waghmare, Director, SGGS IE&T, Nanded and Dr. M.B. Kokare, Coordinator, Center of Excellence in Signal and Image Processing, for their constant encouragement, great support and 24 × 7 open access to state of the art laboratory facilities. Authors are thankful to Dr. Ravindra C. Thool for his constant encouragement. Authors are really very grateful to the referees for their valuable suggestions and comments.
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Sapate, S., Talbar, S. (2016). An Overview of Pectoral Muscle Extraction Algorithms Applied to Digital Mammograms. In: Dey, N., Bhateja, V., Hassanien, A. (eds) Medical Imaging in Clinical Applications. Studies in Computational Intelligence, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-319-33793-7_2
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