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Medical Image Analysis
Volume 14, Issue 2
, Pages 87-110
, April 2010
A review of automatic mass detection and segmentation in mammographic images
References
- Abdel-Dayem, A.R., El-Sakka, M.R., 2005. Fuzzy entropy based detection of suspicious masses in digital mammogram images. In: IEEE Conference on Engineering in Medicine and Biology Society, pp. 4017–4022.
- . PCNN for detection of masses in digital mammogram. Neural Network World. 2006;16(2):129–141
- Allen, B.H., Oxely, M.E., Collins, M.J.A., 2002. A universal segmentation platform for computer-aided detection. In: International Workshop on Digital Mammography, pp. 164–168.
- Altrichter, M., Ludányi, Z., Horváth, G., 2005. Joint analysis of multiple mammographic views in CAD systems for breast cancer detection. In: Lecturer Notes in Computer Science, vol. 3540, pp. 760–769.
- American Cancer Society, 2007. Breast Cancer: Facts and Figures 2007–08, ACS.
- Astley, S.M., Zwiggelaar, R., Parr, T.C., Taylor, C.J., 1998. Prompting in mammography: how good must prompt generators be? In: International Workshop on Digital Mammography, pp. 347–354.
- Attikiouzel, Y., Chandrasekhar, R., 2002. DSP in mammography. In: International Conference on Digital Signal Processing, pp. 29–34.
- Aylward, S.R., Hemminger, B.H., Pisano, E.D., 1998. Mixture modelling for digital mammogram display and analysis. In: International Workshop on Digital Mammography, pp. 305–312.
- Ball, J.E., Butler, T.W., Bruce, L.M., 2004. Towards automated segmentation and classification of masses in digital mammograms. In: IEEE Conference on Engineering in Medicine and Biology Society, pp. 1814–1817.
- Bårman, H., Granlund, G.H., 1994. Computer aided diagnosis of mammograms using a hierarchical framework. In: International Workshop on Digital Mammography, pp. 271–280.
- . Breast Cancer Detection: Mammograms and Other Methods in Breast Imaging. New York: Grune and Stratton; 1987;
- Benois, J., Barba, D., 1992. Image segmentation by region-contour cooperation for image coding. In: IAPR International Conference on Pattern Recognition, vol. C, pp. 331–334.
- . Digital and screen-film mammography: comparison of image acquisition and interpretation times. Am. J. Roentgenol. 2006;187(1):38–41
- Beucher, S., Lenteuejoul, C., 1979. Use of watersheds in contour detection. In: Proceedings of the International Workshop on Image Processing: Real-Time Edge and Motion Detection/Estimation, pp. 2.1–2.12.
- . Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press; 1981;
- . Analysis of cancers missed at screening mammography. Radiology. 1992;184(3):613–617
- . Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection. Radiology. 2001;219(1):192–202
- . Pattern Recognition and Machine Learning. New York: Springer; 2006;
- . Estimation and comparison of CAD system performance in clinical settings. Acad. Radiol. 2005;12:687–694
- . On the comparison of FROC curves in mammography CAD systems. Med. Phys. 2005;32(2):412–417
- . Scene classification using a hybrid generative/discriminative approach. IEEE Trans. Pattern Anal. Machine Intell. 2008;30(4):712–727
- Bovis, K.J., Singh, S., 2000. Detection of masses in mammograms using texture measures. In: IAPR International Conference on Pattern Recognition, vol. 2, pp. 267–270.
- Bruynooghe, M., 2006. Mammographic mass detection using unsupervised clustering in synergy with a parcimonious supervised rule-based classifier. In: Lecturer Notes in Computer Science, vol. 4046, pp. 68–75.
- . An approach to automated detection of tumors in mammograms. IEEE Trans. Med. Imag. 1990;9(3):233–241
- Brzakovic, D., Vujovic, N., Neskovic, M., Brzakovic, P., Fogarty, K., 1994. Mammogram analysis by comparison with previuos screening. In: International Workshop on Digital Mammography, pp. 131–140.
- . Mammography screening matters for young women with breast carcinoma. Cancer. 2003;97(2):352–358
- . Automated analysis of mammographic densities. Phys. Med. Biol. 1996;41(5):909–923
- Calder, B., Clarke, S., Linnett, L.M., Carmichael, D., 1996. Statistical models for the detection of abnormalities in digital mammography. In: IEE Colloquium Digest Mammography, pp. 6/1–6/6.
- . A novel featureless approach to mass detection in digital mammograms based on support vector machines. Phys. Med. Biol. 2004;49:961–975
- Cao, A.Z., Song, Q., Yang, X.L., Liu, S., 2004a. Breast mass segmentation on digital mammograms by a combined deterministic annealing method. In: IEEE International Symposium on Biomedical Imaging, vol. 2, pp. 1303–1306.
- Cao, A.Z., Song, Q., Yang, X.L., Wang, L., 2004b. Breast mass segmentation based on information theory. In: IAPR International Conference on Pattern Recognition, vol. 3, pp. 758–761.
- . Mammogram segmentation by contour searching and mass lesions classification with neural network. IEEE Trans. Nucl. Sci. 2006;53(5):2827–2833
- . Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system. Med. Phys. 2004;31(6):1512–1520
- . Characterization of difference of Gaussian filters in the detection of mammographic regions. Med. Phys. 2006;33(11):4104–4114
- . Computerized localization of breast lesions from two views – an experimental comparison of two methods. Invest. Radiol. 1999;34(9):585–588
- . Computerized identification of suspicious regions for masses in digitized mammograms. Invest. Radiol. 1996;31(3):146–153
- . Robustness of computerized identification of masses in digitized mammograms – a preliminary assessment. Invest. Radiol. 1996;31(9):563–568
- Che, F.N., Fairhust, M.C., Wells, C.P., Hanson, M., 1996. Evaluation of a two-stage model for detection of abnormalities in digital mammograms. In: IEE Colloquium Digest Mammography, pp. 13/1–13/4.
- . On digital mammogram segmentation and microcalcification detection using multiresolution wavelet analysis. Graph. Models Image Process. 1997;59(5):349–364
- . Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm. Comput. Graph. Image Process. 1979;10:172–182
- . Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recogn. 2003;36(12):2967–2991
- . Mass lesion detection with a fuzzy neural network. Pattern Recogn. 2004;37:1189–1200
- . Approaches for automated detection and classification of masses in mammograms. Pattern Recogn. 2006;39(4):646–668
- Christoyianni, I., Constantinou, E., Dermatas, E., 2004. Automatic detection of abnormal tissue in bilateral mammograms using neural networks. In: Methods and Applications of Artificial Intelligence, Helenic Conference on AI, pp. 267–275.
- . Fast detection of masses in computer-aided mammography. IEEE Signal Process. Mag. 2000;17(1):54–64
- Chu, Y., Li, L., Clark, R.A., 2002. Graph-based region growing for mass segmentation in digital mammography. In: Proceedings of SPIE, vol. 4684, pp. 1690–1697.
- Comer, M.L., Liu, S., Delp, E.J., 1996. Statistical segmentation of mammograms. In: International Workshop on Digest Mammography, pp. 475–478.
- Constantinidis, A.S., Fairhust, M.C., Deravi, F., Hanson, M., Wells, C.P., Chapman-Jones, C., 1999. Evaluating classification strategies for detection of circumscribed masses in digital mammograms. In: International Conference on Image Processing and its Application, pp. 435–439.
- . Detection of circumscribed masses in digital mammograms using behaviour–knowledge space method. Electron. Lett. 2000;36(4):302–303
- . A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms. Pattern Recogn. 2001;34(8):1527–1537
- . Machine Vision. second ed.. London, UK: Academic Press; 1997;
- . Nation-wide breast cancer screening in the Netherlands: support for breast cancer mortality reduction. National evaluation team for breast cancer screening. Int. J. Cancer. 1995;60(6):777–780
- . Maximum-likelihood from incomplete data via EM algorithm. J. Roy. Stat. Soc. B. 1977;1–38
- Diahi, J.G., Frouge, C., Giron, A., Fertil, B., 1996. Artificial neural networks for detection of breast cancer in mammography. In: International Workshop on Digest Mammography, pp. 329–334.
- . Pattern Classification. second ed.. New York: John Wiley and Sons; 2001;
- . Evaluation of computer-aided detection systems in the detection of small invasive breast carcinoma. Radiology. 2007;245(1):88–94
- . CADx of mammographic masses and clustered microcalcifications: a review. Med. Phys. 2009;36(6):2052–2068
- . A concentric morphology model for the detection of masses in mammography. IEEE Trans. Med. Imag. 2007;26(6):880–889
- Eurostat, 2002. Health statistics Atlas on mortality in the European Union. Office for Official Publications of the European Union.
- Fauci, F., Bagnasco, S., Bellotti, R., Cascio, D., Cheran, S.C., De Carlo, F., De Nunzio, G., Fantacci, M.E., Forni, G., Lauria, A., Lopez Torrez, E., Magro, R., Masala, G.L., Oliva, P., Quarta, M., Raso, G., Retico, A., Tangaro, S., 2004. Mammogram segmentation by contour searching and massive lesion classification with neural network. In: IEEE Nuclear Science Symposium Conference Record, vol. 5, pp. 2695–2699.
- . A massive lesion detection algorithm in mammography. Phys. Med. 2005;21(1):23–30
- . Influence of computer-aided detection on performance of screening mammography. New Engl. J. Med. 2007;536(14):1399–1409
- . Comparison of similarity measures for the task of template matching of masses on serial mammograms. Med. Phys. 2005;32(2):515–529
- . Screening mammography with computer-aided detection: prospective study of 12860 patients in a community breast center. Radiology. 2001;220:781–786
- Freixenet, J., Muñoz, X., Raba, D., Martı´, J., Cufı´, X., 2002. Yet another survey on image segmentation: region and boundary information integration. In: European Conference on Computer Vision, vol. III, pp. 408–422.
- . Eigendetection of masses considering false positive reduction and breast density information. Med. Phys. 2008;35(5):1840–1853
- . A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1997;55(1):119–139
- . A survey on image segmentation. Pattern Recogn. 1981;13:3–16
- . Differential analysis of bilateral mammograms. Int. J. Pattern Recogn. Artif. Intell. 2003;17(7):1207–1226
- Giger, M.L., Yin, F.F., Doi, K., Wu, Y., Vyborny, C.J., Schmidt, R.A., Huo, Z., 1992. Computerized detection and characterization of mass lesions in digital mammography. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 1370–1372.
- Giménez, V., Manrique, D., Rı´os, J., Vilarrasa, A., 1999. Iterative method for automatic detection of masses in digital mammograms for computer-aided diagnosis. In: Proceedings of SPIE, vol. 3661, pp. 1086–1093.
- Good, W.F., Zheng, B., Chang, Y.H., Wang, X.H., Maitz, G., Gur, D., 1999. Multi-image CAD employing features derived from ipsilateral mammographic views. In: Proceedings of SPIE, vol. 3661, pp. 474–485.
- Good, W.F., Zheng, B., Chang, Y.H., Wang, Z.H., Maitz, G.S., 2001. Generalized procrustean image deformation for substraction of mammograms. In: Proceedings of SPIE, vol. 3661, pp. 1562–1573.
- Goto, M., Morikawa, A., Fujita, H., Hara, T., Endo, T., 1998. Detection of spicules on mammograms based on a multistage pendulum filter. In: International Workshop on Digital Mammography, pp. 135–138.
- Groshong, B.R., Kegelmeyer, W.P., 1996. Evaluation of a Hough transform method for circumscribed lesion detection. In: International Workshop on Digital Mammography, pp. 361–366.
- Guliato, D., Rangayyan, R.M., Carnielli, W.A., Zuffo, J.A., Desautels, J.E.L., 1998. Segmentation of breast tumors in mammograms by fuzzy region growing. In: IEEE Conference on Engineering in Medicine and Biology Society, vol. 20, pp. 1002–1005.
- . Segmentation of breast tumors in mammograms using fuzzy sets. J. Electron. Imag. 2003;12(3):369–378
- Gulsrud, T.O., Engan, K., Hanstveit, T., 2005. Watershed segmentation of detected masses in digital mammograms. In: IEEE Conference on Engineering in Medicine and Biology Society, pp. 3304–3307.
- . The use of texture analysis to identify suspicious masses in mammography. Phys. Med. Biol. 1995;40(5):835–855
- . Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. J. Natl. Cancer Inst. 2004;96(3):185–190
- Hachama, M., Desolneux, A., Richard, F., 2006. A probabilistic approach for the simultaneous mammogram registration and abnormality detection. In: Lecturer Notes in Computer Science, vol. 4046, pp. 205–212.
- . Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses. Med. Phys. 2001;28(11):2309–2638
- . Automated registration of breast lesions in temporal pairs of mammograms for interval change analysis–local affine transformation for improved localization. Med. Phys. 2001;28(6):1070–1079
- . Nonpalpable breast lesions: recommendations for biopsy based on suspicion of carcinoma at mammography. Radiology. 1988;167(2):353–358
- Hassanien, A.E., Ali, J.M., Nobuhara, H., 2004. Detection of spiculated masses in mammograms based on fuzzy image processing. In: Lecturer Notes in Computer Science, vol. 3070, pp. 1002–1007.
- . Development of an automated method for detecting mammographic masses with a partial loss of region. IEEE Trans. Med. Imag. 2001;20(12):1209–1214
- Heath, M.D., Bowyer, K.W., 2000. Mass detection by relative image intensity. In: International Workshop on Digital Mammography, pp. 219–225.
- Hejazi, M.R., Ho, Y.S., 2005. Automated detection of tumors in mammograms using two segments for classification. In: Lecturer Notes in Computer Science, vol. 3767, pp. 910–921.
- Herredsvela, J., Gulsrud, T., Engan, K., 2005. Detection of circumscribed masses in mammograms using morphological segmentation. In: Proceedings of SPIE, vol. 5747, pp. 902–913.
- Heywang-Köbrunner, S.H., Dershaw, D.D., Schreer, I., 2001. Diagnostic Breast Imaging. Mammography, sonography, magnetic resonance imaging, and interventional procedures. Thieme, Stuttgart, Germany.
- Highnam, R., Kita, Y., Brady, M., Shepstone, B., English, R., 1998. Determining correspondence between views. In: International Workshop on Digital Mammography, pp. 111–118.
- Hong, B.W., Brady, M., 2003. A topographic representation for mammogram segmentation. In: Lecturer Notes in Computer Science, vol. 2879, pp. 730–737.
- . Analysis of spiculation in the computerized classification of mammographic masses. Med. Phys. 1995;22(10):1569–1579
- . Data clustering: a review. ACM: Computing Surveys. 1999;31(3):264–323
- . A method for automatic detection of spicules in mammograms. J. Comput. Aided Diagn. Med. Images. 1998;2(4):1–8
- . Prescreening entire mammograms for masses with artificial neural networks: preliminary results. Acad. Radiol. 1997;4(6):405–414
- Karssemeijer, N., 1994. Recognition of stellate lesions in digital mammograms. In: International Workshop on Digital Mammography, pp. 211–219.
- . Automated classification of parenchymal patterns in mammograms. Phys. Med. Biol. 1998;43:365–378
- Karssemeijer, N., 1999. Local orientation distribution as a function of spatial scale for detection of masses in mammograms. In: Proceedings on Information Processing in Medial Imaging, vol. 1613, pp. 280–293.
- . Detection of stellate distortions in mammograms. IEEE Trans. Med. Imag. 1996;15(5):611–619
- Karssemeijer, N., te Brake, G.M., 1998. Combining single view features and asymmetry for detection of mass lesions. In: International Workshop on Digital Mammography, pp. 95–102.
- Kasai, S., Kaji, D., Kano, A., Fujita, H., Hara, T., Endo, T., 2002. Mass detection algorithm for digital mammograms based on an adaptive thresholding techniques utilizing multi-resolution processing. In: International Workshop on Digital Mammography, pp. 334–338.
- Kegelmeyer, W.P., 1992. Computer detection of stellate lesions in mammograms. In: Proceedings of SPIE, vol. 1660, pp. 446–454.
- . Computer-aided mammographic screening for spiculated lesions. Radiology. 1994;191(2):331–337
- Khan, F., Sarma, A., Sun, Y., Tufts, D., 2002. Mass detection using tolerance intervals and a rank detector. In: IEEE International Symposium on Biomedical Imaging, pp. 185–188.
- . Computer-aided detection in the United Kingdom national breast screening programme: prospective study. Radiology. 2005;237(2):444–449
- . Computer-aided detection in full-field digital mammography: sensitivity and reproducibility in serial examinations. Radiology. 2008;246(1):71–80
- . Steepest changes of a probability-based cost function for delineation of mammographic masses: a validation study. Med. Phys. 2004;31(10):2796–2810
- Kinnard, L., Lo, S.C.B., Makariou, E., Osicka, T., Wang, P., Freedman, M.T., Chouikha, M.F., 2004b. Likelihood function analysis for segmentation of mammographic masses for various margin groups. In: IEEE International Symposium on Biomedical Imaging, vol. 1, pp. 113–116.
- Kinnard, L., Lo, S.C.B., Wang, P., Freedman, M.T., Chouikha, M.F., 2002. Automated segmentation of mammographic masses using fuzzy shadow and maximum-likelihood analysis. In: IEEE International Symposium on Biomedical Imaging, pp. 241–244.
- Kita, Y., Highnam, R., Brady, M., 1998. Correspondence between different view breast X-rays using a simulation of breast deformation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 700–707.
- . Correspondence between different view breast X-rays using curved epipolar lines. Comput. Vis. Image Understanding. 2001;83(1):38–56
- Kobatake, H., Murakami, M., 1996. Adaptive filter to detect rounded convex regions: Iris filter. In: IAPR International Conference on Pattern Recognition, vol. 2, pp. 340–345.
- . Computerized detection of malignant tumors on digital mammograms. IEEE Trans. Med. Imag. 1999;18(5):369–378
- Kobatake, H., Takeo, H., Nawano, S., 1998. Tumor detection system for full-digital mammography. In: International Workshop on Digital Mammography, pp. 87–94.
- . Detection of spicules on mammogram based on skeleton analysis. IEEE Trans. Med. Imag. 1996;15(3):235–245
- Kobatake, H., Yoshinaga, Y., Murakami, M., 1994. Automatic detection of malignant tumors on mammogram. In: IEEE International Conference on Image Processing, vol. 1, pp. 407–410.
- Kok-Wiles, S.L., Brady, M., Highman, R., 1998. Comparing mammogram pairs for the detection of lesions. In: International Workshop on Digital Mammography, pp. 103–110.
- . Automated detection of masses in mammograms by local adaptive thresholding. Comput. Biol. Med. 2007;37(1):37–48
- . Breast Imaging. Philadelphia: Lippincott-Raven; 1998;
- Kumar, M.P., Torr, P.H.S., Zisserman, A., 2005. OBJCUT. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 18–25.
- . Automated seeded lesion segmentation on digital mammograms. IEEE Trans. Med. Imag. 1998;17(4):510–517
- . Comparison of full-field digital mammography to screen-film mammography with respect to diagnostic accuracy of lesion characterization in breast tissue biopsy specimens. Acad. Radiol. 2002;9:1378–1382
- Kwok, S.M., Chandrasekhar, R., Attikiouzel, Y., 2002. Adaptation of the Daugman–Downing texture demodulation to highlight circumscribed mass lesions on mammograms. In: International Conference on Digital Signal Processing, pp. 449–452.
- . On techniques for detecting circumscribed masses in mammograms. IEEE Trans. Med. Imag. 1989;8(4):377–386
- Laine, A., Huda, W., Chen, D., Harris, J., 1996. Segmentation of masses using continuous scale representations. In: International Workshop on Digital Mammography, pp. 447–450.
- . Mammographic image processing using wavelet processing techniques. Epidemiol. Rev. 1995;5(5):518–523
- . Automated detection of breast tumors using the asymmetry approach. Comput. Biomed. Res. 1991;24(3):273–295
- . Mammographic mass detection by adaptive thresholding and region growing. Int. J. Imag. Syst. Technol. 2001;11(5):340–346
- . Multiple bilateral masses detected on screening mammography: assessment of need for recall imaging. Am. J. Roentgenol. 2000;175(1):23–29
- . Computerized radiographic mass detection – part I: lesion site selection by morphological enhancement and contextual segmentation. IEEE Trans. Med. Imag. 2001;20(4):289–301
- . Markov random field for tumor detection in digital mammography. IEEE Trans. Med. Imag. 1995;14(3):565–576
- Li, J., Liu, K.J.R., Wang, Y., Lo, S.C.B., 1996a. Morphological filtering and stochastic modeling-based segmentation of masses on mammographic images. In: IEEE Nuclear Science Symposium Conference Record, vol. 3, pp. 1792–1796.
- Li, J., Liu, K.J.R., Wang, Y., Lo, S.C.B., 1996b. Nonlinear filtering enhancement and histogram modeling segmentation of masses for digital mammograms. In: IEEE Conference on Engineering in Medicine and Biology Society, pp. 1045–1046.
- . Computer-aided diagnosis of masses with full-field digital mammography. Acad. Radiol. 2002;9(1):4–12
- . Digital mammography: computer assisted diagnosis method for mass detection with multi-orientation and multiresolution wavelet transform. Acad. Radiol. 1997;4(11):724–731
- Li, L., Qian, W., Clarke, L.P., Clark, R.A., Thomas, J.A., 1999. Improving mass detection by adaptive and multiscale processing in digitized mammograms. In: Proceedings of SPIE, vol. 3661, pp. 490–498.
- . Multiresolution detection of spiculated lesions in digital mammograms. IEEE Trans. Image Process. 2001;10(6):874–884
- . A textural approach for mass false positive reduction in mammography. Comput. Med. Imag. Grap. 2009;33(6):415–422
- . A multiple circular path convolution neural network system for detection of mammographic masses. IEEE Trans. Med. Imag. 2002;21(2):150–158
- . A discrete dynamic contour model. IEEE Trans. Med. Imag. 1995;14:12–24
- MacQueen, J.B., 1967. Some methods of classification and analysis of multivariate observations. In: Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297.
- Martí, J., Freixenet, J., Muñoz, X., Oliver, A., 2003. Active region segmentation of mammographic masses based on texture, contour, and shape features. In: Lecturer Notes in Computer Science, vol. 2652, pp. 478–485.
- Martı´, R., Raba, D., Oliver, A., Zwiggelaar, R., 2006. Mammographic registration: proposal and evaluation of a new approach. In: Lecturer Notes in Computer Science, vol. 4046, pp. 213–220.
- Martı´, R., Zwiggelaar, R., Rubin, C.M.E., 2001. Tracking mammographic structures over time. In: British Machine Vision Conference, pp. 143–152.
- . Automatic point correspondence and registration based on linear structures. Int. J. Pattern Recogn. Artif. Intell. 2002;16(3):331–340
- Matsubara, T., Fujita, H., Hara, T., Kasai, S., Otsuka, O., Hatanaka, Y., Endo, T., 1998. Development of a new algorithm for detection of mammographic masses. In: International Workshop on Digital Mammography, pp. 139–142.
- Matsubara, T., Fujita, H., Kasai, S., Goto, M., Tani, Y., Hara, T., Endo, T., 1997. Development of new schemes for detection and analysis of mammographic masses. In: International Conference on Information Systems, pp. 63–66.
- McKenzie, P., Alder, M., 1994. Initializing the EM algorithm for use in Gaussian mixture modeling. In: Proceedings on Pattern Recognition in Practice, vol. IV, pp. 91–105.
- . Computer aided diagnosis for breast masses detection on a telemammography system. Comp. Med. Imag. Grap. 2003;27:497–502
- . Computerized-aided diagnosis: automatic detection of malignant masses in digitized mammograms. Med. Phys. 1998;25(6):957–964
- Metz, C.E., 1996. Evaluation of digital mammography by ROC analysis. In: International Workshop on Digital Mammography, pp. 61–68.
- Miller, L., Ramsey, N., 1996. The detection of malignant masses by non-linear multiscale analysis. In: International Workshop on Digital Mammography, pp. 335–340.
- Morrison, S., Linnett, L.M., 1999. A model based approach to object detection in digital mammography. In: IEEE International Conference on Image Processing, vol. 2, pp. 182–186.
- . Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert Syst. Appl. 2005;28:713–723
- . Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans. Med. Imag. 2001;20(12):1214–1215
- Nakagawa, T., Hara, T., Fujita, H., Iwase, T., Endo, T., Horita, K., 2004. Automated contour extraction of mammographic mass shadow using an improved active contour model. In: International Congress Series, vol. 1268, pp. 882–885.
- National Electrical Manufacturers Association, 2006. Digital Imaging and Communications in Medicine (DICOM), third ed. National Electrical Manufacturers Association.
- . Automated detection and classification of breast tumors. Comput. Biomed. Res. 1992;25(3):218–237
- . Computer-aided detection, in its present form, is not an effective aid for screening mammography. Med. Phys. 2006;33(4):811–814
- . Dose reduction in full-field digital mammography: an anthropomorphic breast phantom study. Br. J. Radiol. 2003;76(907):478–482
- Öktem, V., Jouny, I., 2004. Automatic detection of malignant tumors in mammograms. In: IEEE Conference on Engineering in Medicine and Biology Society, pp. 1770–1773.
- Oliver, A., Freixenet, J., Martı´, R., Denton, E.R.E., Zwiggelaar, R., 2006. Mammographic mass eigendetection. In: Medical Image Understanding and Analysis, pp. 71–75.
- . A novel breast tissue density classification methodology. IEEE Trans. Inform. Technol. Biomed. 2008;12(1):55–65
- . Mammographic mass detection using a mass template. Korean J. Radiol. 2005;6(4):221–228
- . An adaptive clustering algorithm for image segmentation. IEEE Trans. Syst. Man Cybern. 1992;40(4):901–914
- . Improvement of computerized mass detection on mammograms: fusion of two-view information. Med. Phys. 2002;29(2):238–247
- Parr, T., Astely, S., Boggis, C., 1994. The detection of stellate lesions in digital mammograms. In: International Workshop on Digital Mammography, pp. 231–239.
- . Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. Med. Phys. 1999;26(8):1642–1654
- . An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection. IEEE Trans. Med. Imag. 1996;15(1):59–67
- . Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification. Med. Phys. 1996;23(10):1685–1696
- Petrick, N., Chan, H.P., Wei, D., Sahiner, B., Wei, D., Helvie, M.A., Goodsit, M.M., Adler, D.D., 1995. Automated detection of breast masses on digital mammograms using adaptive density-weighted contrast-enhancement filtering. In: Proceedings of SPIE, vol. 2434, pp. 590–597.
- . Breast cancer detection: evaluation of a mass-detection algorithm for computer-aided diagnosis – experience in 263 patients. Radiology. 2002;224(1):217–224
- Pfisterer, R.S., Aghdasi, F., 1999. Hexagonal wavelets for the detection of masses in digitised mammograms. In: Proceedings of SPIE, vol. 3813, pp. 966–977.
- . Tumor detection in digitized mammograms by image texture analysis. Opt. Eng. 2001;40(2):209–216
- . Quantitative classification of breast tumors in digitized mammograms. Med. Phys. 1996;23(8):1337–1345
- . Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussian and derivative-based feature saliency. IEEE Trans. Med. Imag. 1997;16(6):811–819
- . An ellipse-fitting based method for efficient registration of breast masses on two mammographic views. Med. Phys. 2008;35(2):487–494
- Qi, H., Snyder, W.E., 1998. Lesion detection and characterization in digital mammography by Bézier histograms. In: IEEE Conference on Engineering in Medicine and Biology Society, vol. 2, pp. 1021–1024.
- . Image feature extraction for mass detection in digital mammography: influence of wavelet analysis. Med. Phys. 1999;26(3):402–408
- Qian, W., Li, L., Clarke, L.P., Mao, F., Clark, R.A., 1998a. Adaptive CAD modules for mass detection in digital mammography. In: IEEE Conference on Engineering in Medicine and Biology Society, vol. 2, pp. 1013–1016.
- Qian, W., Li, L., Clarke, L.P., Mao, F., Clark, R.A., Thomas, J., 1998b. A computer assisted diagnostic system for mass detection. In: International Workshop on Digital Mammography, pp. 79–86.
- . Computer-aided mass detection based on ipsilateral multiview mammograms. Acad. Radiol. 2007;14(5):530–538
- . Digital mammography – wavelet transform and Kalman-filtering neural network in mass segmentation and detection. Acad. Radiol. 2001;8(11):1074–1082
- Raba, D., Oliver, A., Martı´, J., Peracaula, M., Espunya, J., 2005. Breast segmentation with pectoral muscle suppression on digital mammograms. In: Lecturer Notes in Computer Science, vol. 3523, pp. 471–478.
- . A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs. J. Frankl. Inst. 2007;344(3–4):312–348
- . Measures of acutance and shape for classification of breast tumors. IEEE Trans. Med. Imag. 1997;16(6):799–810
- . A new image registration technique with free boundary constraints: application to mammography. Comput. Vis. Image Understanding. 2003;89:166–196
- . Machine perception of three-dimensional solids. In: Tippet J, Berkowitz D, Clapp L, Koester C, Vanderburgh A editor. Optical and Electro-Optical Information Processing. Cambridge, MA: MIT Press; 1965;p. 159–197
- . A logic filter for tumor detection on mammograms. J. Comput. Sci. Technol. 2000;15(6):629–632
- Rogova, G.L., Ke, C.C., Acharya, R.S., Stomper, P.C., 1999. Feature choice for detection of cancerous masses by constrained optimization. In: Proceedings of SPIE, vol. 3661, pp. 1440–1447.
- . Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection. Comput. Med. Imag. Graph. 2008;32(4):304–315
- . Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. Proc. IEEE. 1998;86(11):2210–2239
- . Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. Med. Phys. 1998;25(4):516–526
- . Improvement of mammographic mass characterization using spiculation measures and morphological features. Med. Phys. 2001;28(7):1455–1465
- . Image feature selection by a genetic algorithm: application to classification of mass and normal breast tissue. Med. Phys. 1996;23:1671–1684
- . Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization. IEEE Trans. Med. Imag. 2001;20(12):1275–1284
- Sakellaropoulos, F., Skiadopoulos, S., Karahaliou, A., Costaridou, L., Panayiotakis, G., 2006. Using wavelet-based features to identify masses in dense breast parenchyma. In: Lecturer Notes in Computer Science, vol. 4046, pp. 557–564.
- Sallam, M., Bowyer, K., 1994. Registering time sequences of mammograms using a two-dimensional image unwarping technique. In: International Workshop on Digital Mammography, pp. 121–130.
- Sallam, M., Bowyer, K., 1996. Detecting abnormal densities in mammograms by comparison with previous screenings. In: International Workshop on Digital Mammography, pp. 417–420.
- Sameti, M., Ward, R.K., 1996. A fuzzy segmentation algorithm for mammogram partitioning. In: International Workshop on Digital Mammography, pp. 471–474.
- Sameti, M., Ward, R.K., Morgan-Parkes, J., Palcic, B., 1997. A method for detection of malignant masses in digitized mammograms using a fuzzy segmentation algorithm. In: IEEE Conference on Engineering in Medicine and Biology Society, pp. 513–516.
- . A regional registration technique for automated interval change analysis of breast lesions on mammograms. Med. Phys. 1999;26(12):2669–2679
- . Detection of breast cancer tumor based on morphological watershed algorithm. Int. J. Graph. Vision Image Process. 2005;5(5):17–21
- . Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med. Phys. 2008;35(1):280–290
- . Breast cancer screening outcomes in women ages 40–49: clinical experience with service screening using modern mammography. J. Natl. Cancer Inst.: Monogr. 1997;22:99–104
- . Identification of regions of interest in digital mammograms. J. Intell. Syst. 2000;10(2):183–217
- Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W., 2005. Discovering objects and their location in images. In: IEEE International Conference on Computer Vision, vol. 1, pp. 370–377.
- . Fundamentals of digital mammograhpy: physics, technology and practical considerations. Radiol. Manag. 2003;25(5):18–31
- Stamatakis, E.A., Ricketts, I.W., Cairns, A.Y., Walker, C., Preece, P.E., 1996. Detecting abnormalities on mammograms by bilateral comparison. In: IEE Colloquium Digest Mammography, pp. 12/1–12/4.
- Stathaki, T., Constantinides, A.G., 1994. Neural networks and higher order spectra for breast cancer detection. In: IEEE Workshop on Neural Networks and Signal Processing, pp. 473–481.
- Suckling, J., Parker, J., Dance, D.R., Astley, S.M., Hutt, I., Boggis, C.R.M., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S.L., Taylor, P., Betal, D., Savage, J., 1994. The Mammographic Image Analysis Society digital mammogram database. In: International Workshop on Digital Mammography, pp. 211–221.
- . Markov random field-based clustering applied to the segmentation of masses in digital mammograms. Comput. Med. Imag. Graph. 2008;32(6):502–512
- . Ipsilateral-mammogram computer-aided detection of breast cancer. Comp. Med. Imag. Graph. 2004;28(3):151–158
- Sun, X.J., Qian, W., Song, D.S., Clark, R.A., 2001. Ipsilateral multi-view CAD system for mass detection in digital mammography. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 19–26.
- . A hybrid system for detecting masses in mammographic images. IEEE Trans. Instrum. Meas. 2006;55(3):944–952
- Tarassenko, L., Hayton, P., Cernaez, N., Brady, M., 1995. Novelty detection for the identification of masses in mammograms. In: IEEE International Conference on Artificial Neural Networks, pp. 442–447.
- . Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography. Health Technol. Assess. 2005;9(6):1–58
- te Brake, G.M., Karssemeijer, N., 1998. Comparison of three mass detection methods. In: International Workshop on Digital Mammography, pp. 119–126.
- . Single and multiscale detection of masses in digital mammograms. IEEE Trans. Med. Imag. 1999;18(7):628–639
- . Segmentation of suspicious densities in digital mammograms. Med. Phys. 2001;28(2):259–266
- te Brake, G.M., Stoutjesdijk, M.J., Karssemeijer, N., 1999. Discrete dynamic contour model for mass segmentation in digital mammograms. In: Proceedings of SPIE, vol. 3661, pp. 911–919.
- . A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. Med. Phys. 2004;31(5):958–971
- . Interval change analysis to improve computer aided detection in mammography. Med. Image Anal. 2006;10(1):82–95
- . A regional registration method to find corresponding mass lesions in temporal mammogram pairs. Med. Phys. 2005;32(8):2629–2638
- . A segmentation technique to detect masses in dense breast digitized mammograms. J. Digit. Imag. 2002;15(1):210–213
- Torralba, A., Murphy, K.P., Freeman, W.T., 2004. Sharing features: efficient boosting procedures for multiclass object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 762–769.
- . Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information. Med. Phys. 2003;30(8):2123–2130
- Undrill, P., Gupta, R., Henry, S., Downing, M., 1996. Texture analysis and boundary refinement to outline mammography masses. In: IEE Colloquium Digest Mammography, pp. 511–516.
- . Increased mammography use and its impact on earlier breast cancer detection in Vermont. Cancer. 2002;94(8):2160–2168
- van Engeland, S., Karssemeijer, N., 2006. Exploitation of correspondence between CC and MLO views in computer aided mass detection. In: Lecturer Notes in Computer Science, vol. 4046, pp. 237–242.
- . Combining two mammographic projections in a computer aided mass detection method. Med. Phys. 2007;34(3):898–905
- . A comparison of methods for mammogram registration. IEEE Trans. Med. Imag. 2003;22(11):1436–1444
- . Finding corresponding regions of interest in mediolateral oblique and craniocaudal mammographic views. Med. Phys. 2006;33(9):3203–3212
- . Computerized detection of breast masses in digitized mammograms. Comput. Biol. Med. 2007;37(2):214–226
- . Improved mammographic CAD performance using multi-view information: a Bayesian network frameworks. Phys. Med. Biol. 2009;54(5):1131–1147
- Velthuizen, R.P., 2000. Computer diagnosis of mammographic masses. In: Workshop on Applied Imagery Pattern Recognition, pp. 166–172.
- . Watershed in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Machine Intell. 1991;13(6):583–598
- Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518.
- Vujovic, N., Bakic, P.R., Brzakovic, D., 1996. Detection of potentially cancerous signs by mammogram followup. In: International Workshop on Digital Mammography, pp. 421–424.
- . Establishing the correspondence between control points in pairs of mammographic images. IEEE Trans. Med. Imag. 1997;6(10):1388–1399
- . Computer vision and artificial intelligence in mammography. Am. J. Roentgenol. 1994;162(3):699–708
- Wai, L.C.C., Brady, M., 2005. Curvilinear structure based mammographic registration. In: Lecturer Notes in Computer Science, vol. 3765, pp. 261–270.
- Wang, K., Qin, H., Fisher, P.R., Zhao, W., 2006. Automatic registration of mammograms using texture-based anisotropic features. In: IEEE International Symposium on Biomedical Imaging, pp. 864–867.
- . Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology. 2000;215(2):554–562
- Wei, D., Sahiner, B., Chan, H.P., Petrick, N., 1994. Detection of masses on mammograms using a convolution neural network. In: International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. 3483–3486.
- . Dual system approach to computer-aided detection of breast masses on mammograms. Med. Phys. 2006;33(11):4157–4168
- . Computer-aided detection of breast masses on mammograms: dual system approach with two-view analysis. Med. Phys. 2009;36(10):4451–4460
- . Computer-aided detection systems for breast masses: comparison of performances on full-field digital mammograms and digitized screen-film mammograms. Acad. Radiol. 2007;14(6):659–669
- . Computer-aided detection of breast masses on full field digital mammograms. Med. Phys. 2005;32(9):2827–2838
- . Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology. 1967;89(2):211–215
- Wirth, M.A., Narhan, J., Gray, D., 2002. Nonrigid mammogram registration using mutual information. In: Proceedings of SPIE, vol. 4684, pp. 95–102.
- Woods, K.S., Bowyer, K.W., 1994. Computer detection of stellate lesions. In: International Workshop on Digital Mammography, pp. 221–229.
- . Bilateral analysis based false positive reduction for computer-aided mass detection. Med. Phys. 2007;34(8):3334–3344
- Xie, M., 2002. A method of tumors detection in digital mammography. In: IEEE International Conference on Communications, Circuits and Systems and West Sino Expositions, vol. 2, pp. 1007–1011.
- Xie, M., Ma, Z., 2001. A method of automatic detection of tumors in mammogram. In: Proceedings of SPIE, vol. 4556, pp. 145–153.
- . Screening mammography-detected cancers: sensitivity of a computer-aided detection system applied to full-field digital mammograms. Radiology. 2007;244(1):104–111
- . Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images. Med. Phys. 1991;18(5):955–963
- . Computerized detection of masses in digital mammograms: automated alignment of breast images and its effect on bilateral-subtraction technique. Med. Phys. 1994;21(3):445–452
- . Comparison of bilateral-substraction and single-image processing techniques in the computerized detection of mammographic masses. Invest. Radiol. 1993;28(6):473–481
- Yin, L., Deshpande, S., Chang, J.K., 2003. Automatic lesion/tumor detection using intelligent mesh-based active contour. In: IEEE International Conference on Tools with Artificial Intelligence, pp. 390–397.
- . Evaluating computer-aided detection algorithms. Med. Phys. 2007;34(6):2024–2038
- Youssry, N., Abou-Chadi, F.E.Z., El-Sayad, A.M., 2003. Early detection of masses in digitized mammograms using texture features and neuro-fuzzy model. In: IEEE Conference on Information Technology in Applied Biomedicine, pp. 226–229.
- . A dual-stage method for lesion segmentation on digital mammograms. Med. Phys. 2007;34(11):4180–4193
- . Computer aided detection of breast masses from digitized mammograms. IEICE Trans. Inform. Syst. 2006;E89D(6):1955–1961
- Zhang, H., Foo, S.W., Krishnan, S.M., Thng, C.H., 2004. Automated breast masses segmentation in digitized mammograms. In: IEEE International Workshop in Biomedical Circuits and System, pp. S2.2–1–S2.2–4.
- Zhang, M., Giger, M.L., Vyborny, C.J., Doi, K., 1996. Mammographic texture analysis for the detection of spiculated lesions. In: International Workshop on Digital Mammography, pp. 347–350.
- . Computerized detection of masses in digitized mammograms: comparison of single-image segmentation and bilateral-image substraction. Acad. Radiol. 1995;2(12):1056–1061
- . Computerized detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis. Acad. Radiol. 1995;2(11):959–966
- . Mass detection in digitized mammograms using two independent computer-assisted diagnosis schemes. Am. J. Roentgenol. 1996;167(6):1421–1424
- . Feature selection for computerized mass detection in digitized mammograms by using a genetic algorithm. Acad. Radiol. 1999;6(6):327–332
- . Performance change of mammographic CAD schemes optimized with most-recent and prior image databases. Acad. Radiol. 2003;10(3):283–288
- . Computer-aided detection in mammography: a reproducibility assessment – initial experience. Radiology. 2003;228(1):58–62
- . Multiview-based computer-aided detection scheme for breast masses. Med. Phys. 2006;33(9):3135–3143
- . An artificial intelligent algorithm for tumor detection in screening mammogram. IEEE Trans. Med. Imag. 2001;20(7):559–567
- . Detection of cancerous masses for screening mammography using DWT based multiresolution Markov Random Field. J. Digit. Imag. 1999;12(1–2):18–23
- . Computerized image analysis: estimation of breast density on mammograms. Med. Phys. 2001;28(6):1056–1069
- . Region competition: unifying snakes, region growing, and Bayes/MDL for multi-band image segmentation. IEEE Trans. Pattern Anal. Machine Intell. 1996;18(9):884–900
- Zouras, W.K., Giger, M.L., Lu, P., Wolverton, D.E., Vyborny, C.J., Doi, K., 1996. Investigation of a temporal subtraction scheme for computerized detection of breast masses in mammograms. In: International Workshop on Digital Mammography, pp. 411–415.
- . Region growing: childhood and adolescence. Comput. Graph. Image Process. 1976;5:382–399
- Zwiggelaar, R., Astley, S.M., Taylor, C.J., 1998. Detecting the central mass of a spiculated lesion using scale-orientation signatures. In: International Workshop on Digital Mammography, pp. 63–70.
- . Model-based detection of spiculated lesions in mammograms. Med. Image Anal. 1999;3(1):39–62
- Zwiggelaar, R., Taylor, C.J., Rubin, C.M.E., 1999. Detection of the central mass of spiculated lesions – signature normalisation and model data aspects. In: Lecturer Notes in Computer Science, vol. 1613, pp. 406–411.
PII: S1361-8415(09)00149-2
doi: 10.1016/j.media.2009.12.005
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Medical Image Analysis
Volume 14, Issue 2
, Pages 87-110
, April 2010
