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Merhof, Dorit

Biomedical Engineering / Biomedizinische Technik

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering and the German Society of Biomaterials

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Habibović, Pamela / Haueisen, Jens / Jahnen-Dechent, Wilhelm / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Leonhardt, Steffen / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Boenick, Ulrich / Jaramaz, Branislav / Kraft, Marc / Lenarz, Thomas / Lenthe, Harry / Lo, Benny / Mainardi, Luca / Micera, Silvestro / Penzel, Thomas / Robitzki, Andrea A. / Schaeffter, Tobias / Snedeker, Jess G. / Sörnmo, Leif / Sugano, Nobuhiko / Werner, Jürgen /

IMPACT FACTOR 2018: 1.007
5-year IMPACT FACTOR: 1.390

CiteScore 2018: 1.24

SCImago Journal Rank (SJR) 2018: 0.282
Source Normalized Impact per Paper (SNIP) 2018: 0.831

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Volume 64, Issue 1


Volume 57 (2012)

A cost-sensitive Bayesian combiner for reducing false positives in mammographic mass detection

Ehsan Kozegar / Mohsen Soryani
Published Online: 2017-10-23 | DOI: https://doi.org/10.1515/bmt-2017-0032


Mammography is the most widely used modality for early breast cancer detection. This work proposes a new computer-aided mass detection approach, in which a denoising method called BM3D is first applied to mammograms. Afterwards, using an adaptive segmentation algorithm, images are segmented to suspicious regions of interest (ROIs) and then a classifier is used to understand the features of true positive (TP) and false positive (FP) patterns. In this way, from selected suspicious ROIs, fractal dimension, texture and intensity features are extracted. Subsequently, a discretization approach followed by correlation-based feature selection (CFS) is combined with a genetic algorithm to obtain the most representative features. To neutralize the classifier’s bias in favor of the major class in imbalanced datasets, an oversampling algorithm is used. In the next step, a cost-sensitive ensemble classifier based on a trainable combiner is proposed in order to reduce the number of FP samples. Finally, the presented method is validated on miniMIAS and INBreast datasets. The free-response receiver operating characteristic (FROC) analysis results prove the efficiency of the proposed approach. A sensitivity of 88% and false positive per image (FPpI) of 0.78 for miniMIAS and also a sensitivity of 86% and FPpI of 0.75 for INBreast dataset were obtained.

Keywords: classification; detection; mammography; mass


  • [1]

    Alpaydin E. Introduction to machine learning. Chapter 15. Cambridge, MA: MIT Press 2004: 364–366.Google Scholar

  • [2]

    Alpaydin E. Introduction to machine learning. Chapter 3. Cambridge, MA: MIT Press 2004: 48–55.Google Scholar

  • [3]

    Anitha J, Peter JD, Pandian SIA. A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms. Comput Methods Programs Biomed 2017; 138: 93–104.Web of SciencePubMedCrossrefGoogle Scholar

  • [4]

    Breiman L. Bagging predictors. Mach Learning 1996; 24: 123–140.CrossrefGoogle Scholar

  • [5]

    Breiman L. Stacked regression. Mach Learning 1996; 24: 49–64.CrossrefGoogle Scholar

  • [6]

    Breiman L. Random forests. Mach Learning 2001; 45: 5–32.CrossrefGoogle Scholar

  • [7]

    Chawla N. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 2002; 16: 321–357.CrossrefGoogle Scholar

  • [8]

    Christoyanni I, Dermatas E, Kokkinakis G. Fast detection of masses in computer aided mammography. IEEE Signal Process Mag 2000; 17: 54–64.CrossrefGoogle Scholar

  • [9]

    Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans Image Process 2007; 16: 1–16.Web of ScienceGoogle Scholar

  • [10]

    Dehungle N, Carneiro G, Bradley AP. Automated mass detection in mammograms using cascaded deep learning and random forests. In: IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA). Adelaide, SA, Australia: IEEE 2015: 1–8.Google Scholar

  • [11]

    Domingos P. A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining. New York: ACM Press 1999: 155–164.Google Scholar

  • [12]

    Dominguez A, Nandi A. Detection of masses in mammograms via statistically based enhancement, multi-level thresholding, and region selection. Comput Med 2008; 32: 304–315.Google Scholar

  • [13]

    Eltonsy NH, Tourassi GD, Elmaghraby AS. A concentric morphology model for the detection of masses in mammography. IEEE Trans Med Imaging 2007; 26: 880–889.Web of ScienceCrossrefPubMedGoogle Scholar

  • [14]

    Fauci F, Raso G, Magro R, et al. A massive lesion detection algorithm in mammography. Phys Med 2005; 21: 23–30.CrossrefPubMedGoogle Scholar

  • [15]

    Fayyad U, Irani K. Multi-interval discretization of continuous valued attributes for classification learning. In: 13th International Joint Conference on Artificial Intelligence (IJCAI). San Mateo, CA 1993: 1022–1027.Google Scholar

  • [16]

    Freund Y, Schapire RE. Experiments with a new boosting algorithm. In: 13th International Conference on Machine Learning. San Francisco 1996: 148–156.Google Scholar

  • [17]

    Gedik N, Atasoy N. A computer aided diagnosis system for breast cancer detection by using a curvelet transform. Turk J Elec Eng Comp Sci 2013; 21: 1002–1014.Google Scholar

  • [18]

    Hall M. Correlation-based feature subset selection for machine learning. PhD thesis, University of Waikato, Hamilton, New Zealand, 1999.Google Scholar

  • [19]

    Hall M, Smith L. Feature selection for machine learning: comparing correlation-based filter approach to the wrapper. In: 12th International Florida Artificial Intelligence Research Society Conference (FLAIRS-99). Orlando, FL, USA 1999.Google Scholar

  • [20]

    Haralick RM, Shanmugan K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; 3: 610–621.Google Scholar

  • [21]

    Hussain M, Khan N. Automatic mass detection in mammograms using multiscale spatial weber local descriptors. In: 19th International Conference on Systems, Signals and Image Processing (IWSSIP). Vienna, Austria: IEEE 2012: 288–291.Google Scholar

  • [22]

    Kobatake H, Murakami M, Takeo H, Nawano S. Computerized detection of malignant tumors on digital mammograms. IEEE Trans Med Imag 1999; 18: 369–378.CrossrefGoogle Scholar

  • [23]

    Kom G, Tiedeu A, Kom M. Automated detection of masses in mammograms by local adaptive thresholding. Comput Biol Med 2007; 37: 37–48.Web of ScienceCrossrefPubMedGoogle Scholar

  • [24]

    Kooi T, Litjens G, van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 2017; 35: 303–312.PubMedCrossrefWeb of ScienceGoogle Scholar

  • [25]

    Kozegar E, Soryani M, Domingues I. A new local adaptive mass detection algorithm in mammograms. In: International Conference on Bio-inspired Systems and Signal Processing. Barcelona, Spain 2013: 133–137.Google Scholar

  • [26]

    Kozegar E, Soryani M, Minaei B, Domingues I. Assessment of a novel mass detection algorithm in mammograms. J Cancer Res Ther 2013; 9: 592–600.Web of ScienceCrossrefPubMedGoogle Scholar

  • [27]

    Kuncheva LI. Combining pattern classifiers: methods and algorithms. Chapter 3. Hoboken, NJ: John Wiley & Sons Inc 2004: 101–109.Google Scholar

  • [28]

    Kuncheva LI. Combining pattern classifiers: methods and algorithms. Chapter 2. Hoboken, NJ: John Wiley & Sons Inc 2004: 88–95.Google Scholar

  • [29]

    Li N, Zhou HJ, Guo Q, Yang Y. A cost sensitive cascaded method for automatic mass detection. In: International Conference on Systems, Man and Cybernetics. Singapore: IEEE 2008: 3454–3458.Google Scholar

  • [30]

    Li Y, Chen H, Cheng L, Cao L. A bilateral analysis scheme for false positive reduction in mammogram mass detection. Comput Biol Med 2015; 57: 84–95.CrossrefPubMedWeb of ScienceGoogle Scholar

  • [31]

    Malagelada AO. Automatic mass segmentation in mammographic images. PhD thesis, Department of Electronics, Computer Science and Automatic Control, University of De Girona, Girona, Spain, 2007Google Scholar

  • [32]

    Masotti M, Campanini R. Texture classification using invariant ranklet features. Pattern Recognit Lett 2008; 29: 1980–1986.CrossrefWeb of ScienceGoogle Scholar

  • [33]

    Melville P, Mooney RJ. Constructing diverse classifier ensembles using artificial training examples. In: 13th International Joint Conference on Artificial Intelligence, Acapulco, Mexico 2003: 505–510.Google Scholar

  • [34]

    Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. Towards a full field digital mammographic database. Acad Radiol 2012; 19: 236–248.Google Scholar

  • [35]

    Nguyen VD, Nguyen DT, Nguyen TD, Thi N, Tran DH. A program for locating possible breast masses on mammograms. In: Proceeding of the 3rd International Conference on the Development of BME. Vietnam: Springer 2010: 11–14.Google Scholar

  • [36]

    Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002; 24: 971–978.CrossrefGoogle Scholar

  • [37]

    Oliver A, Freixenet J, Marti J, et al. A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 2010; 14: 87–110.CrossrefPubMedWeb of ScienceGoogle Scholar

  • [38]

    Oliviera MD, Braz J, Cardoso P, Gattass M. Detection of masses in digital mammograms using K-means and support vector machine. Electron Lett Comput Vis Image Anal 2009; 8: 39–50.CrossrefGoogle Scholar

  • [39]

    Petrick N, Chan H, Sahiner B, Wei D. An adaptive density weighted contrast enhancement filter for mammographic breast mass detection. IEEE Trans Med Imaging 1996; 15: 59–67.PubMedCrossrefGoogle Scholar

  • [40]

    Polakowski WE, Cournoyer D, Rogers SK, et al. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussian and derivative-based feature saliency. IEEE Trans Med Imaging 1997; 16: 811–819.CrossrefGoogle Scholar

  • [41]

    Rodriguez JJ, Kuncheva LI, Alonso CJ. Random forests. IEEE Trans Pattern Anal Mach Intell 2006; 28: 1619–1630.Google Scholar

  • [42]

    Smyth, Wolpert D. An evaluation of linearity combining density estimators via stacking. Mach Learn 1999; 36: 59–83.CrossrefGoogle Scholar

  • [43]

    Soh L, Tsatsoulis C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 1999; 73: 780–795.Google Scholar

  • [44]

    Sree SV, Ng EYK, Acharya RU, Faust O. Breast imaging: a survey. World J Clin Oncol 2011; 2: 171–178.PubMedCrossrefGoogle Scholar

  • [45]

    Suckling J. The mammographic image analysis society digital mammogram database. In: Exerpta Medica International Congress Series, vol. 1069. 1994: 375–378.Google Scholar

  • [46]

    Tai S, Chen Z, Tsai W. An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Inform 2013; 18: 618–627.Web of ScienceGoogle Scholar

  • [47]

    Varela C, Tahoces P, Mendez A, Souto M, Vidal J. Computerized detection of breast masses in digitized mammograms. Comput Biol Med 2007; 37: 214–226.PubMedWeb of ScienceCrossrefGoogle Scholar

  • [48]

    Vikhe P, Thool V. Mass detection in mammographic images using wavelet processing and adaptive threshold technique. J Med Syst 2016; 40: 1–16.Web of ScienceGoogle Scholar

  • [49]

    Yang S, Wang C, Chung Y. A computer aided system for mass detection and classification in digitized mammograms. Biomed Eng App Basis Commun 2005; 17: 215–228.CrossrefGoogle Scholar

About the article

Received: 2017-03-23

Accepted: 2017-09-20

Published Online: 2017-10-23

Published in Print: 2019-02-25

Citation Information: Biomedical Engineering / Biomedizinische Technik, Volume 64, Issue 1, Pages 39–52, ISSN (Online) 1862-278X, ISSN (Print) 0013-5585, DOI: https://doi.org/10.1515/bmt-2017-0032.

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