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Volumn 5, Issue 4, 2015, Pages 653-674

Computer aided diagnosis system for mammogram analysis: A survey

Author keywords

Breast cancer; Classification; Feature extraction; Mammogram; Preprocessing; Segmentation

Indexed keywords

ACTIVE CONTOUR MODEL; ADAPTIVE NEIGHBORHOOD CONTRAST ENHANCEMENT; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; BREAST CANCER; CANCER CLASSIFICATION; CANCER DIAGNOSIS; COMPUTER AIDED DESIGN; COMPUTER AIDED DIAGNOSIS; CONTRAST ENHANCEMENT; CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION; DATA MINING; DECISION TREE; DIAGNOSTIC ACCURACY; DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY; DISCRIMINANT ANALYSIS; FUZZY SYSTEM; GRADIENT ENHANCEMENT; GRAY LEVEL COOCCURRENCE MATRIX; GRAY LEVEL DIFFERENCE METHOD; GRAY LEVEL RUN LENGTH METHOD; HISTOGRAM; HISTOGRAM EQUALIZATION; IMAGE PROCESSING; K NEAREST NEIGHBOR; LINEAR DISCRIMINANT ANALYSIS; MACHINE LEARNING; MAMMOGRAM IMAGE ANALYSIS SOCIETY; MAMMOGRAPHY; MARKOV RANDOM FIELD; PATTERN RECOGNITION; RADIAL BASED FUNCTION; REFERENCE DATABASE; REVIEW; SHAPE FEATURE; SUPPORT VECTOR MACHINE; SURROUNDING REGION DEPENDENCE METHOD; WATERSHED;

EID: 84931060319     PISSN: 21567018     EISSN: 21567026     Source Type: Journal    
DOI: 10.1166/jmihi.2015.1441     Document Type: Review
Times cited : (14)

References (214)
  • 1
    • 84903647885 scopus 로고    scopus 로고
    • Computer-aided detection (CAD) of breast masses in mammography: Combined detection and ensemble classification
    • J. Y. Choi, D. H. Kim, K. N. Plataniotis, and Y. M. Ro, Computer-aided detection (CAD) of breast masses in mammography: Combined detection and ensemble classification, Phys. Med. Biol. 59, 3697 (2014).
    • (2014) Phys. Med. Biol. , vol.59 , pp. 3697
    • Choi, J.Y.1    Kim, D.H.2    Plataniotis, K.N.3    Ro, Y.M.4
  • 3
    • 84878012105 scopus 로고    scopus 로고
    • An improved data mining technique for classification and detection of breast cancer from mammograms
    • A. K. Mohanty, M. R. Senapati, and S. K. Lenka, An improved data mining technique for classification and detection of breast cancer from mammograms, Neural Comput. and Applic. 22, 303 (2013).
    • (2013) Neural Comput. and Applic. , vol.22 , pp. 303
    • Mohanty, A.K.1    Senapati, M.R.2    Lenka, S.K.3
  • 4
    • 81555214178 scopus 로고    scopus 로고
    • Texture-based analysis of clustered microcalcifications detected on mammograms
    • A. Tiedeu, C. Daul, A. Kentsop, P. Graebling, and D. Wolf, Texture-based analysis of clustered microcalcifications detected on mammograms, Digital Signal Processing 22, 124 (2012).
    • (2012) Digital Signal Processing , vol.22 , pp. 124
    • Tiedeu, A.1    Daul, C.2    Kentsop, A.3    Graebling, P.4    Wolf, D.5
  • 5
    • 84878341877 scopus 로고    scopus 로고
    • Data mining techniques for breast cancer detection in thermograms using hybrid feature extraction strategy
    • M. R. K. Mookiah, U. R. Acharya, and E. Y. K. Ng, Data mining techniques for breast cancer detection in thermograms using hybrid feature extraction strategy, Quantitative Infrared Thermography Journal 9, 151 (2012).
    • (2012) Quantitative Infrared Thermography Journal , vol.9 , pp. 151
    • Mookiah, M.R.K.1    Acharya, U.R.2    Ng, E.Y.K.3
  • 6
    • 84904014206 scopus 로고    scopus 로고
    • Application of infrared thermography in computer aided diagnosis
    • O. Faust, U. R. Acharya, and E. Y. K. Ng, Application of infrared thermography in computer aided diagnosis, Infrared Physics and Technology 66, 160 (2014).
    • (2014) Infrared Physics and Technology , vol.66 , pp. 160
    • Faust, O.1    Acharya, U.R.2    Ng, E.Y.K.3
  • 9
    • 75649114239 scopus 로고    scopus 로고
    • Classifier with simplified learning phase for detecting microcalcifications in digital mammograms
    • I. Zyout, I. Abdel-Qader, C. Jacobs, and C. Bayesian, Classifier with simplified learning phase for detecting microcalcifications in digital mammograms. International J. Biomedical Imaging 1 (2009).
    • (2009) International J. Biomedical Imaging , pp. 1
    • Zyout, I.1    Abdel-Qader, I.2    Jacobs, C.3    Bayesian, C.4
  • 10
    • 78651532026 scopus 로고    scopus 로고
    • A new ensemble learning approach for microcalcification clusters detection
    • X. Zhang, A new ensemble learning approach for microcalcification clusters detection. J. Software 4, 1014 (2009).
    • (2009) J. Software , vol.4 , pp. 1014
    • Zhang, X.1
  • 11
    • 59249098260 scopus 로고    scopus 로고
    • Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis
    • L. Wei, Y. Yang, and R. M. Nishikawa, Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis. Pattern Recognition 42, 1126 (2009).
    • (2009) Pattern Recognition , vol.42 , pp. 1126
    • Wei, L.1    Yang, Y.2    Nishikawa, R.M.3
  • 17
    • 19344363582 scopus 로고    scopus 로고
    • Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines
    • A. Papadopoulos, D. I. Fotiadis, and A. Likas, Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artificial Intelligence in Medicine 34, 141 (2005).
    • (2005) Artificial Intelligence in Medicine , vol.34 , pp. 141
    • Papadopoulos, A.1    Fotiadis, D.I.2    Likas, A.3
  • 20
    • 83755204867 scopus 로고    scopus 로고
    • Soft computing based decision making approach for tumor mass identification in mammogram
    • M. Bhattacharya and A. Das, Soft computing based decision making approach for tumor mass identification in mammogram. International J. Bioinformatics Research 1, 37 (2009).
    • (2009) International J. Bioinformatics Research , vol.1 , pp. 37
    • Bhattacharya, M.1    Das, A.2
  • 22
    • 0025296795 scopus 로고
    • Automatic computer detection of clustered calcifications in digital mammograms
    • D. H. Davies and D. R. Dance, Automatic computer detection of clustered calcifications in digital mammograms. Phys. Med. Biol. 35, 1111 (1990).
    • (1990) Phys. Med. Biol. , vol.35 , pp. 1111
    • Davies, D.H.1    Dance, D.R.2
  • 23
    • 0030996609 scopus 로고    scopus 로고
    • Computerized classification of malignant and benign microcalcifications on mammograms: Texture analysis using an artificial neural network
    • H. P. Chan, B. Sahiner, N. Petrick, M. A. Helvie, K. L. Lam, D. Adler, and M. Goodsitt, Computerized classification of malignant and benign microcalcifications on mammograms: Texture analysis using an artificial neural network. Phys. Med. Biol. 42, 549 (1997).
    • (1997) Phys. Med. Biol. , vol.42 , pp. 549
    • Chan, H.P.1    Sahiner, B.2    Petrick, N.3    Helvie, M.A.4    Lam, K.L.5    Adler, D.6    Goodsitt, M.7
  • 24
    • 80054732949 scopus 로고    scopus 로고
    • A comparative study of image features for classification of breast microcalcifications
    • I. I. Andreadis, G. M. Spyrou, and K. S. Nikita, A comparative study of image features for classification of breast microcalcifications. Meas. Sci. Technol. 22, 1 (2011).
    • (2011) Meas. Sci. Technol. , vol.22 , pp. 1
    • Andreadis, I.I.1    Spyrou, G.M.2    Nikita, K.S.3
  • 25
    • 4544361200 scopus 로고    scopus 로고
    • Comparison of multiwavelet, wavelet, Haralick and shape features for microcalcification classification in mammograms
    • H. Soltanian-Zadeh, F. Rafiee-Rad, and S. Pourabdollah-Nejad, Comparison of multiwavelet, wavelet, Haralick and shape features for microcalcification classification in mammograms. Pattern Recognition 37, 1973 (2004).
    • (2004) Pattern Recognition , vol.37 , pp. 1973
    • Soltanian-Zadeh, H.1    Rafiee-Rad, F.2    Pourabdollah-Nejad, S.3
  • 27
    • 71249094411 scopus 로고    scopus 로고
    • Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer
    • B. Verma, P. McLeod, and A. Klevansky, Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer. Expert Systems with Applications 37, 3344 (2010).
    • (2010) Expert Systems with Applications , vol.37 , pp. 3344
    • Verma, B.1    McLeod, P.2    Klevansky, A.3
  • 28
    • 77951125531 scopus 로고    scopus 로고
    • Using BI-RADS descriptors and ensemble learning for classifying masses in mammograms
    • MCBR-CDS 2009
    • Y. Zhang, N. Tomuro, J. Furst, and D. S. Raicu, Using BI-RADS descriptors and ensemble learning for classifying masses in mammograms. MCBR-CDS 2009, LNCS 5853, 69 (2010).
    • (2010) LNCS , vol.5853 , pp. 69
    • Zhang, Y.1    Tomuro, N.2    Furst, J.3    Raicu, D.S.4
  • 30
    • 0034267949 scopus 로고    scopus 로고
    • Image processing algorithms for digital mammography: A pictorial essay
    • E. D. Pisano, Image processing algorithms for digital mammography: A pictorial essay. Radiographics 20, 1479 (2000).
    • (2000) Radiographics , vol.20 , pp. 1479
    • Pisano, E.D.1
  • 34
  • 35
    • 77954642214 scopus 로고    scopus 로고
    • Classification of breast tumors on digital mammogram using Law's texture features
    • MICCAI 2001
    • C. Varela, N. Karssemeijer, and P. G. Tahoces, Classification of breast tumors on digital mammogram using Law's texture features. MICCAI 2001, LNCS 2208, 1391 (2001).
    • (2001) LNCS , vol.2208 , pp. 1391
    • Varela, C.1    Karssemeijer, N.2    Tahoces, P.G.3
  • 37
    • 17344393687 scopus 로고    scopus 로고
    • A segmentation technique to detect masses in dense breast digitized mammograms
    • V. T. Santos, H. Schiabel, C. E. Goes, and R. H. Benatti, A segmentation technique to detect masses in dense breast digitized mammograms. J. Digital Imaging 51, 210 (2002).
    • (2002) J. Digital Imaging , vol.51 , pp. 210
    • Santos, V.T.1    Schiabel, H.2    Goes, C.E.3    Benatti, R.H.4
  • 41
    • 2142711004 scopus 로고    scopus 로고
    • Mass lesion detection with a fuzzy neural network
    • H. D. Cheng and M. Cui, Mass lesion detection with a fuzzy neural network. Pattern Recognition 37, 1189 (2004).
    • (2004) Pattern Recognition , vol.37 , pp. 1189
    • Cheng, H.D.1    Cui, M.2
  • 42
    • 2442666562 scopus 로고    scopus 로고
    • A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography
    • S. Timp and N. Karssemeijer, A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. Med. Phys. 31, 958 (2004).
    • (2004) Med. Phys. , vol.31 , pp. 958
    • Timp, S.1    Karssemeijer, N.2
  • 43
    • 1842664403 scopus 로고    scopus 로고
    • Ipsilateral-mammogram computer-aided detection of breast cancer
    • X. Sun, W. Qian, and D. Song, Ipsilateral-mammogram computer-aided detection of breast cancer. Comput. Med. Imag. and Graphics 28, 151 (2004).
    • (2004) Comput. Med. Imag. and Graphics , vol.28 , pp. 151
    • Sun, X.1    Qian, W.2    Song, D.3
  • 46
    • 17844381877 scopus 로고    scopus 로고
    • Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural
    • R. Mousa, Q. Munib, and A. Moussa, Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert Systems with Applications 28, 713 (2005).
    • (2005) Expert Systems with Applications , vol.28 , pp. 713
    • Mousa, R.1    Munib, Q.2    Moussa, A.3
  • 48
    • 33746659809 scopus 로고    scopus 로고
    • A completely automated CAD system for mass detection in a large mammographic database
    • R. Bellotti, A completely automated CAD system for mass detection in a large mammographic database. Med. Phy. 33, 3066 (2006).
    • (2006) Med. Phy. , vol.33 , pp. 3066
    • Bellotti, R.1
  • 50
    • 32044457119 scopus 로고    scopus 로고
    • Approaches for automated detection and classification of masses in mammograms
    • H. D. Cheng, X. J. Shi, R. Min, L. M. Hu, X. P. Cai, and H. N. Du, Approaches for automated detection and classification of masses in mammograms. Pattern Recognition 39, 646 (2006).
    • (2006) Pattern Recognition , vol.39 , pp. 646
    • Cheng, H.D.1    Shi, X.J.2    Min, R.3    Hu, L.M.4    Cai, X.P.5    Du, H.N.6
  • 51
    • 33749375744 scopus 로고    scopus 로고
    • A ranklet-based image representation for mass classification in digital mammograms
    • M. Masotti, A ranklet-based image representation for mass classification in digital mammograms. Med. Phys. 33, 3951 (2006).
    • (2006) Med. Phys. , vol.33 , pp. 3951
    • Masotti, M.1
  • 52
    • 33744535368 scopus 로고    scopus 로고
    • Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers
    • M. E. Mavroforakis, H. V. Georgiou, N. Dimitropoulos, D. Cavouras, and S. Theodoridis, Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artificial Intelligence in Medicine 37, 145 (2006).
    • (2006) Artificial Intelligence in Medicine , vol.37 , pp. 145
    • Mavroforakis, M.E.1    Georgiou, H.V.2    Dimitropoulos, N.3    Cavouras, D.4    Theodoridis, S.5
  • 53
    • 28744432535 scopus 로고    scopus 로고
    • Interval change analysis to improve computer aided detection in mammography
    • S. Timp and N. Karssemeijer, Interval change analysis to improve computer aided detection in mammography. Medical Image Analysis 10, 82 (2006).
    • (2006) Medical Image Analysis , vol.10 , pp. 82
    • Timp, S.1    Karssemeijer, N.2
  • 55
    • 34547591212 scopus 로고    scopus 로고
    • Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier
    • P. Delogu, M. E. Fantacci, P. Kasae, and A. Retico, Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier. Computers in and Biology and Medicine 37, 1479 (2007).
    • (2007) Computers in and Biology and Medicine , vol.37 , pp. 1479
    • Delogu, P.1    Fantacci, M.E.2    Kasae, P.3    Retico, A.4
  • 56
    • 37249035594 scopus 로고    scopus 로고
    • Classification of breast masses in mammogram images using Ripley's K function and support vector machine
    • MLDM 2007
    • L. Martins, G. B. Junior, A. C. Silva, A. C. Paiva, and M. Gattass, Classification of breast masses in mammogram images using Ripley's K function and support vector machine. MLDM 2007,LNAI 4571, 784 (2007).
    • (2007) LNAI , vol.4571 , pp. 784
    • Martins, L.1    Junior, G.B.2    Silva, A.C.3    Paiva, A.C.4    Gattass, M.5
  • 58
    • 37849017515 scopus 로고    scopus 로고
    • Contourlet-based mammography mass classification
    • ICIAR 2007
    • F. Moayedi, Z. Azimifar, R. Boostani, and S. Katebi, Contourlet-based mammography mass classification. ICIAR 2007,LNCS 4633, 923 (2007).
    • (2007) LNCS , vol.4633 , pp. 923
    • Moayedi, F.1    Azimifar, Z.2    Boostani, R.3    Katebi, S.4
  • 60
    • 35248866593 scopus 로고    scopus 로고
    • Classification of mammographic masses using support vector machines and Bayesian networks
    • M. Samulski, N. Karssemeijer, P. Lucasand, and P. Groot, Classification of mammographic masses using support vector machines and Bayesian networks, Proceeding of SPIE (2007), p. 6514.
    • Proceeding of SPIE (2007) , pp. 6514
    • Samulski, M.1    Karssemeijer, N.2    Lucasand, P.3    Groot, P.4
  • 65
    • 44149089793 scopus 로고    scopus 로고
    • Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers
    • T. Mu, A. K. Nandi, and R. M. Rangayyan, Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers. J. Digital Imaging 21, 153 (2008).
    • (2008) J. Digital Imaging , vol.21 , pp. 153
    • Mu, T.1    Nandi, A.K.2    Rangayyan, R.M.3
  • 66
    • 54049098747 scopus 로고    scopus 로고
    • Markov random field-based clustering applied to the segmentation of masses in digital mammograms
    • M. Suliga, R. Deklerck, and E. Nyssen, Markov random field-based clustering applied to the segmentation of masses in digital mammograms. Comput. Med. Imag. and Graphics 32, 502 (2008).
    • (2008) Comput. Med. Imag. and Graphics , vol.32 , pp. 502
    • Suliga, M.1    Deklerck, R.2    Nyssen, E.3
  • 67
    • 56749165404 scopus 로고    scopus 로고
    • Segmentation technique for detecting suspect masses in dense breast digitized images as a tool for mammography CAD schemes
    • H. Schiabel, V. T. Santos, and M. F. Angelo, Segmentation technique for detecting suspect masses in dense breast digitized images as a tool for mammography CAD schemes. ACM Special Interest Group on Applied Computing 1333 (2008).
    • (2008) ACM Special Interest Group on Applied Computing , vol.1333
    • Schiabel, H.1    Santos, V.T.2    Angelo, M.F.3
  • 68
    • 41949128301 scopus 로고    scopus 로고
    • Evaluating the effect of image preprocessing on an information theoretic CAD system in mammography
    • G. D. Tourassi, R. Ike, S. Singh, and B. Harrawood, Evaluating the effect of image preprocessing on an information theoretic CAD system in mammography. Acad. Radiol. 15, 626 (2008).
    • (2008) Acad. Radiol. , vol.15 , pp. 626
    • Tourassi, G.D.1    Ike, R.2    Singh, S.3    Harrawood, B.4
  • 69
    • 37649010087 scopus 로고    scopus 로고
    • Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms
    • B. Verma, Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms. Artificial Intelligence in Medicine 42, 67 (2008).
    • (2008) Artificial Intelligence in Medicine , vol.42 , pp. 67
    • Verma, B.1
  • 70
    • 79960001502 scopus 로고    scopus 로고
    • Preliminary diagnostics of mammograms using moments and texture features
    • M. Eisa, M. Refaat, and A. F. El-Gamal, Preliminary diagnostics of mammograms using moments and texture features. ICGST-GVIP J 9, 21 (2009).
    • (2009) ICGST-GVIP J , vol.9 , pp. 21
    • Eisa, M.1    Refaat, M.2    El-Gamal, A.F.3
  • 71
    • 79952757441 scopus 로고    scopus 로고
    • Automatic detection of breast cancers in mammograms using structured support vector machines
    • D. Wang, L. Shi, and P. A. Heng, Automatic detection of breast cancers in mammograms using structured support vector machines. Neurocomputing 72, 3296 (2009).
    • (2009) Neurocomputing , vol.72 , pp. 3296
    • Wang, D.1    Shi, L.2    Heng, P.A.3
  • 74
    • 67649664255 scopus 로고    scopus 로고
    • Development of tolerant features for characterization of masses in mammograms
    • A. Rojas-Dominguez and A. K. Nandi, Development of tolerant features for characterization of masses in mammograms. Computers in Biology and Medicine 39, 678 (2009).
    • (2009) Computers in Biology and Medicine , vol.39 , pp. 678
    • Rojas-Dominguez, A.1    Nandi, A.K.2
  • 76
    • 67349156354 scopus 로고    scopus 로고
    • A novel soft cluster neural network for the classification of suspicious areas in digital mammograms
    • B. Verma, P. McLeod, and A. Klevansky, A novel soft cluster neural network for the classification of suspicious areas in digital mammograms. Pattern Recognition 42, 1845 (2009).
    • (2009) Pattern Recognition , vol.42 , pp. 1845
    • Verma, B.1    McLeod, P.2    Klevansky, A.3
  • 77
    • 70350134243 scopus 로고    scopus 로고
    • Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms
    • S. Yoon and S. Kim, Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms. Pattern Recognition Letters 30, 1489 (2009).
    • (2009) Pattern Recognition Letters , vol.30 , pp. 1489
    • Yoon, S.1    Kim, S.2
  • 78
    • 59449110510 scopus 로고    scopus 로고
    • Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers
    • B. Zheng, Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers. Acad. Radiol. 16, 266 (2009).
    • (2009) Acad. Radiol. , vol.16 , pp. 266
    • Zheng, B.1
  • 79
    • 77049125483 scopus 로고    scopus 로고
    • A comparison of two methods for the segmentation of masses in the digital mammograms
    • R. B. Dubey, M. Hanmandlu, and S. K. Gupta, A comparison of two methods for the segmentation of masses in the digital mammograms. Comput. Med. Imag. and Graphics 34, 185 (2010).
    • (2010) Comput. Med. Imag. and Graphics , vol.34 , pp. 185
    • Dubey, R.B.1    Hanmandlu, M.2    Gupta, S.K.3
  • 81
    • 84863220843 scopus 로고    scopus 로고
    • Diagnosing breast masses in digital mammography using feature selection and ensemble methods
    • S.-T. Luo and B.-W. Cheng, Diagnosing breast masses in digital mammography using feature selection and ensemble methods. J. Med. Sys. 36, 569 (2010).
    • (2010) J. Med. Sys. , vol.36 , pp. 569
    • Luo, S.-T.1    Cheng, B.-W.2
  • 83
    • 77958472400 scopus 로고    scopus 로고
    • Subclass fuzzy-svm classifier as an efficient method to enhance the mass detection in mammograms
    • F. Moayedi, R. Boostani, A. R. Kazemi, S. Katebi, and E. Dashti, Subclass fuzzy-svm classifier as an efficient method to enhance the mass detection in mammograms, Iranian J. Fuzzy Systems 7, 15 (2010).
    • (2010) Iranian J. Fuzzy Systems , vol.7 , pp. 15
    • Moayedi, F.1    Boostani, R.2    Kazemi, A.R.3    Katebi, S.4    Dashti, E.5
  • 85
    • 77957878979 scopus 로고    scopus 로고
    • Effect of pixel resolution on texture of breast masses in mammograms
    • R. M. Rangayyan, T. M. Nguyen, F. J. Ayres, and A. K. Nandi, Effect of pixel resolution on texture of breast masses in mammograms. J. Digital Imaging 23, 547 (2010).
    • (2010) J. Digital Imaging , vol.23 , pp. 547
    • Rangayyan, R.M.1    Nguyen, T.M.2    Ayres, F.J.3    Nandi, A.K.4
  • 87
    • 84860254276 scopus 로고    scopus 로고
    • An improved medical decision support system to identify the breast cancer using mammogram
    • M. Suganthi and M. Madheswaran, An improved medical decision support system to identify the breast cancer using mammogram. J. Med. Syst. 36, 79 (2010).
    • (2010) J. Med. Syst. , vol.36 , pp. 79
    • Suganthi, M.1    Madheswaran, M.2
  • 88
    • 77957658513 scopus 로고    scopus 로고
    • Computer-aided detection-The effect of training databases on detection of subtle breast masses
    • B. Zheng, X. Wang, D. Lederman, J. Tan, and D. Gur, Computer-aided detection-The effect of training databases on detection of subtle breast masses. Acad, Radiol. 17, 1401 (2010).
    • (2010) Acad, Radiol. , vol.17 , pp. 1401
    • Zheng, B.1    Wang, X.2    Lederman, D.3    Tan, J.4    Gur, D.5
  • 90
    • 79151475545 scopus 로고    scopus 로고
    • Expert system based on neuro-fuzzy rules for diagnosis breast cancer
    • A. Keles, A. Keles, and U. Yavuz, Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Experts Systems with Applications 38, 5719 (2011).
    • (2011) Experts Systems with Applications , vol.38 , pp. 5719
    • Keles, A.1    Keles, A.2    Yavuz, U.3
  • 94
    • 84867299673 scopus 로고    scopus 로고
    • Prediction of breast cancer using artificial neural networks
    • I. Saritas, Prediction of breast cancer using artificial neural networks. J. Med. Sys. 36, 2901 (2011).
    • (2011) J. Med. Sys. , vol.36 , pp. 2901
    • Saritas, I.1
  • 95
    • 83855162264 scopus 로고    scopus 로고
    • A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation
    • M. M. Eltoukhy, I. Faye, and B. B. Samir, A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation. Computers in Biology and Medicine 42, 123 (2012).
    • (2012) Computers in Biology and Medicine , vol.42 , pp. 123
    • Eltoukhy, M.M.1    Faye, I.2    Samir, B.B.3
  • 97
    • 84880762552 scopus 로고    scopus 로고
    • Mass classification method in mammograms using correlated association rule mining
    • A. K. Mohanty, M. R. Senapati, B. Beberta, and S. K. Lenka, Mass classification method in mammograms using correlated association rule mining. Neural Comput. and Applic. 23, 273 (2012).
    • (2012) Neural Comput. and Applic. , vol.23 , pp. 273
    • Mohanty, A.K.1    Senapati, M.R.2    Beberta, B.3    Lenka, S.K.4
  • 98
    • 84876446495 scopus 로고    scopus 로고
    • A novel image mining technique for classification of mammograms using hybrid feature selection
    • A. K. Mohanty, M. R. Senapati, and S. K. Lenka, A novel image mining technique for classification of mammograms using hybrid feature selection. Neural Comput. and Applic. 22, 1151 (2012).
    • (2012) Neural Comput. and Applic. , vol.22 , pp. 1151
    • Mohanty, A.K.1    Senapati, M.R.2    Lenka, S.K.3
  • 99
  • 100
    • 84896917808 scopus 로고    scopus 로고
    • An alternative approach to reduce massive false positivies in mammograms using block variance of local coefficients features and support vector machine
    • M. P. Nguyen, Q. D. Truong, D. T. Nguyen, T. D. Nguyen, and V. D. Nguyen, An alternative approach to reduce massive false positivies in mammograms using block variance of local coefficients features and support vector machine. Procedia Computer Science 20, 399 (2013).
    • (2013) Procedia Computer Science , vol.20 , pp. 399
    • Nguyen, M.P.1    Truong, Q.D.2    Nguyen, D.T.3    Nguyen, T.D.4    Nguyen, V.D.5
  • 101
    • 84892613237 scopus 로고    scopus 로고
    • Saliency based mass detection from screening mammograms
    • P. Agrawal, M. Vasta, and R. Singh, Saliency based mass detection from screening mammograms. Signal Processing 99, 29 (2014).
    • (2014) Signal Processing , vol.99 , pp. 29
    • Agrawal, P.1    Vasta, M.2    Singh, R.3
  • 103
    • 0034498682 scopus 로고    scopus 로고
    • Off-line mammography screening system embedded with hierarchically-coarse-to-fine techniques for the detection and segmentation of clustered microcalcifications
    • C.-S. Lo, P.-C. Chung, S. K. Lee, C. I. Chang, T. Lee, G.-C. Hsu, and C.-W. Yang, Off-line mammography screening system embedded with hierarchically-coarse-to-fine techniques for the detection and segmentation of clustered microcalcifications. IEICE Trans. Inf. Syst. 83, 2161 (2000).
    • (2000) IEICE Trans. Inf. Syst. , vol.83 , pp. 2161
    • Lo, C.-S.1    Chung, P.-C.2    Lee, S.K.3    Chang, C.I.4    Lee, T.5    Hsu, G.-C.6    Yang, C.-W.7
  • 105
    • 0033624996 scopus 로고    scopus 로고
    • A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films
    • S. Yu and L. Guan, A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Transactions on Medical Imaging 19, 115 (2000).
    • (2000) IEEE Transactions on Medical Imaging , vol.19 , pp. 115
    • Yu, S.1    Guan, L.2
  • 107
    • 0034969334 scopus 로고    scopus 로고
    • Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography
    • C. Markopoulos, E. Kouskos, K. Koufopoulos, V. Kyriakou, and J. Gogas, Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography. European J. Radiology 39, 60 (2001).
    • (2001) European J. Radiology , vol.39 , pp. 60
    • Markopoulos, C.1    Kouskos, E.2    Koufopoulos, K.3    Kyriakou, V.4    Gogas, J.5
  • 109
    • 0035263429 scopus 로고    scopus 로고
    • A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques
    • B. Verma and J. Zakos, A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Transactions on Information Technology in Biomedicine 5, 46 (2001).
    • (2001) IEEE Transactions on Information Technology in Biomedicine , vol.5 , pp. 46
    • Verma, B.1    Zakos, J.2
  • 112
    • 0042876899 scopus 로고    scopus 로고
    • Computer-aided classification of breast microcalcification clusters: Merging of features from image processing and radiologists
    • J. Y. Lo, M. K. Gavrielides, and J. L. Jesneck, Computer-aided classification of breast microcalcification clusters: Merging of features from image processing and radiologists, Proceeding of SPIE 5032, 882 (2003).
    • (2003) Proceeding of SPIE , vol.5032 , pp. 882
    • Lo, J.Y.1    Gavrielides, M.K.2    Jesneck, J.L.3
  • 113
    • 0347596501 scopus 로고    scopus 로고
    • Segmentation and feature extraction for reliable classification of microcalcifications in digital mammograms
    • A. Wroblewska, P. Boninski, A. Przellaskowski, and M. Kazubek, Segmentation and feature extraction for reliable classification of microcalcifications in digital mammograms. Opto-electronics Review 11, 227 (2003).
    • (2003) Opto-electronics Review , vol.11 , pp. 227
    • Wroblewska, A.1    Boninski, P.2    Przellaskowski, A.3    Kazubek, M.4
  • 115
    • 24344463437 scopus 로고    scopus 로고
    • Image segmentation feature selection and pattern classification for mammographic microcalcifications
    • J. C. Fu, S. K. Lee, S. T. C. Wong, J. Y. Yeh, A. H. Wang, and H. K. Wu, Image segmentation feature selection and pattern classification for mammographic microcalcifications. Comput. Med. Imag. and Graphics 29, 419 (2005).
    • (2005) Comput. Med. Imag. and Graphics , vol.29 , pp. 419
    • Fu, J.C.1    Lee, S.K.2    Wong, S.T.C.3    Yeh, J.Y.4    Wang, A.H.5    Wu, H.K.6
  • 117
    • 19044383038 scopus 로고    scopus 로고
    • A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications
    • L. Wei, Y. Yang, R. M. Nishikawa, and Y. Jiang, A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Transactions on Medical Imaging 24, 371 (2005).
    • (2005) IEEE Transactions on Medical Imaging , vol.24 , pp. 371
    • Wei, L.1    Yang, Y.2    Nishikawa, R.M.3    Jiang, Y.4
  • 118
    • 17444397485 scopus 로고    scopus 로고
    • Neural Vs, statistical classifier in conjunction with genetic algorithm based feature selection
    • P. Zhang, B. Verma, and K. Kumar, Neural Vs, statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recognition Letters 26, 909 (2005).
    • (2005) Pattern Recognition Letters , vol.26 , pp. 909
    • Zhang, P.1    Verma, B.2    Kumar, K.3
  • 120
    • 33646077220 scopus 로고    scopus 로고
    • Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis
    • Y. Peng, B. Yao, and J. Jiang, Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis. Artificial Intelligence in Medicine 37, 43 (2006).
    • (2006) Artificial Intelligence in Medicine , vol.37 , pp. 43
    • Peng, Y.1    Yao, B.2    Jiang, J.3
  • 122
    • 33744520648 scopus 로고    scopus 로고
    • Detection of microcalcifications in digital mammograms using wavelet filter and Markov random field model
    • S.-N. Yu, K.-Y. Li, and Y.-K. Huang, Detection of microcalcifications in digital mammograms using wavelet filter and Markov random field model. Comput. Med. Imag. and Graphics 30, 163 (2006).
    • (2006) Comput. Med. Imag. and Graphics , vol.30 , pp. 163
    • Yu, S.-N.1    Li, K.-Y.2    Huang, Y.-K.3
  • 123
    • 33746359388 scopus 로고    scopus 로고
    • Calcification descriptor and relevance feedback learning algorithms for content-based mammogram retrieval
    • IWDM 2006
    • C.-H. Wei and C.-T. Li, Calcification descriptor and relevance feedback learning algorithms for content-based mammogram retrieval. IWDM 2006, LNCS 4046, 307 (2006).
    • (2006) LNCS , vol.4046 , pp. 307
    • Wei, C.-H.1    Li, C.-T.2
  • 124
    • 34247150397 scopus 로고    scopus 로고
    • Computer aided detection system for clustered microcalcifications: Comparison of performance on full field digital mammograms and digitized screen-film mammograms
    • J. Ge, L. M. Hadjiiski, B. Sahiner, J. Wei, M. A. Helvie, C. Zhou, and H.-P. Chan, Computer aided detection system for clustered microcalcifications: Comparison of performance on full field digital mammograms and digitized screen-film mammograms. Phys. Med. Biol. 52, 981 (2007).
    • (2007) Phys. Med. Biol. , vol.52 , pp. 981
    • Ge, J.1    Hadjiiski, L.M.2    Sahiner, B.3    Wei, J.4    Helvie, M.A.5    Zhou, C.6    Chan, H.-P.7
  • 125
    • 33947383729 scopus 로고    scopus 로고
    • Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks
    • S. Halkiotis, T. Botsis, and M. Rangoussi, Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks. Signal Processing 87, 1559 (2007).
    • (2007) Signal Processing , vol.87 , pp. 1559
    • Halkiotis, S.1    Botsis, T.2    Rangoussi, M.3
  • 126
    • 84931081837 scopus 로고    scopus 로고
    • Comparison of class separability, forward sequential search and genetic algorithms for feature selection in the classification of individual and clustered microcalcifications in digital mammograms
    • R. R. Hernandez-Cisneros, H. Terashima-Marin, and S. Conant-Pablos, Comparison of class separability, forward sequential search and genetic algorithms for feature selection in the classification of individual and clustered microcalcifications in digital mammograms, International Conference in Image Analysis and Recognition Canada (2007).
    • International Conference in Image Analysis and Recognition Canada (2007)
    • Hernandez-Cisneros, R.R.1    Terashima-Marin, H.2    Conant-Pablos, S.3
  • 128
    • 34249941777 scopus 로고    scopus 로고
    • Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications
    • M. Karnan and K. Thangavel, Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications. Computer Methods and Programs in Biomedicine 87, 12 (2007).
    • (2007) Computer Methods and Programs in Biomedicine , vol.87 , pp. 12
    • Karnan, M.1    Thangavel, K.2
  • 129
    • 33846829794 scopus 로고    scopus 로고
    • A novel neural-genetic algorithm to find the most significant combination of digital mammograms
    • B. Verma and P. Zhang, A novel neural-genetic algorithm to find the most significant combination of digital mammograms. Applied Soft Computing 7, 612 (2007).
    • (2007) Applied Soft Computing , vol.7 , pp. 612
    • Verma, B.1    Zhang, P.2
  • 130
    • 54949158747 scopus 로고    scopus 로고
    • Computer-based identification of breast cancer using digitized mammograms
    • U. R. Acharya, E. Y. K. Ng, Y. H. Chang, J. Yang, and G. J. L. Kaw, Computer-based identification of breast cancer using digitized mammograms. J. Med. Syst. 32, 499 (2008).
    • (2008) J. Med. Syst. , vol.32 , pp. 499
    • Acharya, U.R.1    Ng, E.Y.K.2    Chang, Y.H.3    Yang, J.4    Kaw, G.J.L.5
  • 132
    • 51849117172 scopus 로고    scopus 로고
    • Dual-energy digital mammography for calcification imaging: Noise reduction techniques
    • S. C. Kappadath and C. C. Shaw, Dual-energy digital mammography for calcification imaging: Noise reduction techniques. Phys. Med. Biol. 53, 5421 (2008).
    • (2008) Phys. Med. Biol. , vol.53 , pp. 5421
    • Kappadath, S.C.1    Shaw, C.C.2
  • 134
    • 56449098759 scopus 로고    scopus 로고
    • A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms
    • N. R. Pal, B. Bhowmick, S. K. Patel, S. Pal, and J. Das, A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms. Neurocomputing 71, 2625 (2008).
    • (2008) Neurocomputing , vol.71 , pp. 2625
    • Pal, N.R.1    Bhowmick, B.2    Patel, S.K.3    Pal, S.4    Das, J.5
  • 135
    • 53049094780 scopus 로고    scopus 로고
    • Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques
    • A. Papadopoulos, D. I. Fotiadis, and L. Costaridou, Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Computers in Biology and Medicine 38, 1045 (2008).
    • (2008) Computers in Biology and Medicine , vol.38 , pp. 1045
    • Papadopoulos, A.1    Fotiadis, D.I.2    Costaridou, L.3
  • 139
    • 78651524032 scopus 로고    scopus 로고
    • MCs detection with combined image features and twin support vector machines
    • X. Zhang, X. Gao, and Y. Wang, MCs detection with combined image features and twin support vector machines. J. Computers 4, 215 (2009).
    • (2009) J. Computers , vol.4 , pp. 215
    • Zhang, X.1    Gao, X.2    Wang, Y.3
  • 140
    • 77950187218 scopus 로고    scopus 로고
    • Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features
    • S.-N. Yu, K.-Y. Li, and Y.-K. Huang, Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features. Expert Systems with Applications 37, 5461 (2010).
    • (2010) Expert Systems with Applications , vol.37 , pp. 5461
    • Yu, S.-N.1    Li, K.-Y.2    Huang, Y.-K.3
  • 144
    • 84859953180 scopus 로고    scopus 로고
    • Comparing the performance of image enhancement methods to detect microcalcification clusters in digital mammography
    • H. Moradmand, S. Setayeshi, A. R. Karimian, M. Sirous, and M. E. Akbari, Comparing the performance of image enhancement methods to detect microcalcification clusters in digital mammography. Iran J. Cancer Prev. 2, 61 (2012).
    • (2012) Iran J. Cancer Prev. , vol.2 , pp. 61
    • Moradmand, H.1    Setayeshi, S.2    Karimian, A.R.3    Sirous, M.4    Akbari, M.E.5
  • 145
    • 84884293967 scopus 로고    scopus 로고
    • Mammographic image denoising and enhancement using the anscombe transformation, adaptive wiener filtering, and the modulation transfer function
    • L. C. S. Ramualdo, M. A. C. Vieira, H. Schiabel, N. D. A. Mascarenhas, and L. R. Borges, Mammographic image denoising and enhancement using the anscombe transformation, adaptive wiener filtering, and the modulation transfer function. J Digit. Imaging 26, 183 (2012).
    • (2012) J Digit. Imaging , vol.26 , pp. 183
    • Ramualdo, L.C.S.1    Vieira, M.A.C.2    Schiabel, H.3    Mascarenhas, N.D.A.4    Borges, L.R.5
  • 146
    • 84877600915 scopus 로고    scopus 로고
    • Automated feature set selection and its application to MCC identification indigital mammograms for breast cancer detection
    • Y. J. Huang, D. Y. Chan, D. C. Cheng, Y. J., P. P. Tsai, W. C. Shen, and R. F. Chen, Automated feature set selection and its application to MCC identification indigital mammograms for breast cancer detection. Sensors 13, 4855 (2012).
    • (2012) Sensors , vol.13 , pp. 4855
    • Huang, Y.J.1    Chan, D.Y.2    Cheng, D.C.3    J, Y.4    Tsai, P.P.5    Shen, W.C.6    Chen, R.F.7
  • 149
    • 33745332155 scopus 로고    scopus 로고
    • Diagnosis of breast cancer in digital mammograms using independent component analysis and neural networks
    • CIARP 2005
    • L. F. A. Campos, A. C. Silva, and A. K. Barros, Diagnosis of breast cancer in digital mammograms using independent component analysis and neural networks. CIARP 2005, LNCS 3773, 460 (2009).
    • (2009) LNCS , vol.3773 , pp. 460
    • Campos, L.F.A.1    Silva, A.C.2    Barros, A.K.3
  • 151
    • 15744379052 scopus 로고    scopus 로고
    • Significance analysis of qualitative mammographic features, using linear classifiers, neural networks and support vector machines
    • M. Mavroforakis, H. Georgiou, N. Dimitropoulos, D. Cavouras, and S. Theodoridis, Significance analysis of qualitative mammographic features, using linear classifiers, neural networks and support vector machines. European J. Radiology 54, 80 (2005).
    • (2005) European J. Radiology , vol.54 , pp. 80
    • Mavroforakis, M.1    Georgiou, H.2    Dimitropoulos, N.3    Cavouras, D.4    Theodoridis, S.5
  • 152
    • 33750702140 scopus 로고    scopus 로고
    • Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network
    • ICONIP 2006
    • R. Panchal and B. Verma, Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network. ICONIP 2006, LNCS 4234, 127 (2006).
    • (2006) LNCS , vol.4234 , pp. 127
    • Panchal, R.1    Verma, B.2
  • 154
    • 33744805830 scopus 로고    scopus 로고
    • Rough set approach for classification of breast cancer mammogram images
    • A. E. Hassanien and J. M. H. Ali, Rough set approach for classification of breast cancer mammogram images. LNCS 2955, 224 (2006).
    • (2006) LNCS , vol.2955 , pp. 224
    • Hassanien, A.E.1    Ali, J.M.H.2
  • 155
    • 33749842760 scopus 로고    scopus 로고
    • Computer aided classification of mammographic tissue using independent component analysis and support vector machines
    • ICANN 2006
    • A. Koutras, Christoyianni, G. Georgoulas, and E. Dermatas, Computer aided classification of mammographic tissue using independent component analysis and support vector machines. ICANN 2006, LNCS 4132, 568 (2006).
    • (2006) LNCS , vol.4132 , pp. 568
    • Koutras, A.1    Christoyianni2    Georgoulas, G.3    Dermatas, E.4
  • 156
    • 33947526434 scopus 로고    scopus 로고
    • Independent component analysis and neural networks applied for classification of malignant, benign and normal tissue in digital mammography
    • L. F. A. Campos, A. C. Silva, and A. K. Barros, Independent component analysis and neural networks applied for classification of malignant, benign and normal tissue in digital mammography. Methods Inf. Med. 46, 212 (2007).
    • (2007) Methods Inf. Med. , vol.46 , pp. 212
    • Campos, L.F.A.1    Silva, A.C.2    Barros, A.K.3
  • 157
    • 33845214650 scopus 로고    scopus 로고
    • Fuzzy rough sets hybrid scheme for breast cancer detection
    • A. E. Hassanien, Fuzzy rough sets hybrid scheme for breast cancer detection. Image and Vision Computing 25, 172 (2007).
    • (2007) Image and Vision Computing , vol.25 , pp. 172
    • Hassanien, A.E.1
  • 161
    • 60849125380 scopus 로고    scopus 로고
    • Associative classification of mammograms using weighted rules
    • S. Dua, H. Singh, and H. W. Thompson, Associative classification of mammograms using weighted rules. Expert Systems with Applications 36, 9250 (2009).
    • (2009) Expert Systems with Applications , vol.36 , pp. 9250
    • Dua, S.1    Singh, H.2    Thompson, H.W.3
  • 163
    • 82755182021 scopus 로고    scopus 로고
    • Comparative study on feature extraction method for breast cancer classification
    • R. Nithya and B. Santhi, Comparative study on feature extraction method for breast cancer classification. J. Theoretical and Applied Information Technology 33, 220 (2011).
    • (2011) J. Theoretical and Applied Information Technology , vol.33 , pp. 220
    • Nithya, R.1    Santhi, B.2
  • 165
    • 83755228864 scopus 로고    scopus 로고
    • Intuitionistic fuzzy c-means and decision tree approach for breast cancer detection and classification
    • S. Shanthi and V. Murali Bhaskaran, Intuitionistic fuzzy c-means and decision tree approach for breast cancer detection and classification. European J. Scientific Research 66, 345 (2011).
    • (2011) European J. Scientific Research , vol.66 , pp. 345
    • Shanthi, S.1    Murali Bhaskaran, V.2
  • 167
    • 84864946394 scopus 로고    scopus 로고
    • Breast cancer diagnosis system based on contourlet analysis and support vector machine
    • S. Dehghani and M. A. Dezfooli, Breast cancer diagnosis system based on contourlet analysis and support vector machine. World Appl. Sci. J. 13, 1067 (2011).
    • (2011) World Appl. Sci. J. , vol.13 , pp. 1067
    • Dehghani, S.1    Dezfooli, M.A.2
  • 168
    • 80955158411 scopus 로고    scopus 로고
    • Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions
    • J. F. Ramirez-Villegas and D. F. Ramirez-Moreno, Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. Neurocomputing 77, 82 (2012).
    • (2012) Neurocomputing , vol.77 , pp. 82
    • Ramirez-Villegas, J.F.1    Ramirez-Moreno, D.F.2
  • 170
    • 84904339110 scopus 로고    scopus 로고
    • Automated diagnosis of mammogram images of breast cancer using discrete wavelet transform and spherical wavelet transform features: A comparative study
    • K. Ganesan, U. R. Acharya, C. K. Chua, C. M. Lim, and K. T. Abraham, Automated diagnosis of mammogram images of breast cancer using discrete wavelet transform and spherical wavelet transform features: A comparative study. Technology in Cancer Research and Treatment 13 (2014).
    • (2014) Technology in Cancer Research and Treatment , vol.13
    • Ganesan, K.1    Acharya, U.R.2    Chua, C.K.3    Lim, C.M.4    Abraham, K.T.5
  • 173
    • 79551681197 scopus 로고    scopus 로고
    • A comparison of breast tissue classification techniques
    • MICCAI 2006
    • A. Oliver, J. Freixenet, R. Marti, and R. Zwiggelaar A comparison of breast tissue classification techniques. MICCAI 2006, LNCS 491, 872 (2006).
    • (2006) LNCS , vol.491 , pp. 872
    • Oliver, A.1    Freixenet, J.2    Marti, R.3    Zwiggelaar, R.4
  • 175
    • 33845329825 scopus 로고    scopus 로고
    • Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms
    • H. S. Sheshadri and A. Kandaswamy, Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. Comput. Med. Imag. and Graphics 31, 46 (2007).
    • (2007) Comput. Med. Imag. and Graphics , vol.31 , pp. 46
    • Sheshadri, H.S.1    Kandaswamy, A.2
  • 176
    • 36148988424 scopus 로고    scopus 로고
    • Semiautomatic mammographic parenchymal patterns classification using multiple statistical features
    • C. Castella, K. Kinkel, M. P. Eckstein, P.-E. Sottas, F. R. Verdun, and F. O. Bochud, Semiautomatic mammographic parenchymal patterns classification using multiple statistical features. Acad. Radiol. 14, 1486 (2007).
    • (2007) Acad. Radiol. , vol.14 , pp. 1486
    • Castella, C.1    Kinkel, K.2    Eckstein, M.P.3    Sottas, P.-E.4    Verdun, F.R.5    Bochud, F.O.6
  • 181
    • 79954609832 scopus 로고    scopus 로고
    • Automatic breast density segmentation: An integration of different approaches
    • M. G. J. Kallenberg, M. Lokate, G. H. V. Gils, and N. Karssemeijer, Automatic breast density segmentation: An integration of different approaches. Phys. Med. Biol. 56, 2715 (2011).
    • (2011) Phys. Med. Biol. , vol.56 , pp. 2715
    • Kallenberg, M.G.J.1    Lokate, M.2    Gils, G.H.V.3    Karssemeijer, N.4
  • 183
    • 84931031367 scopus 로고    scopus 로고
    • Mammogrphic image based breast tissue classification with kernel self-contained fisher discriminant for breast cancer diagnosis
    • J. B. Li, Mammogrphic image based breast tissue classification with kernel self-contained fisher discriminant for breast cancer diagnosis. J. Med. Syst. (2011)
    • (2011) J. Med. Syst.
    • Li, J.B.1
  • 185
    • 84876115364 scopus 로고    scopus 로고
    • Background intensity independent texture features for assessing breast cancer risk in screening mammograms
    • X. Z. Li, S. Williams, and M. J. Bottema, Background intensity independent texture features for assessing breast cancer risk in screening mammograms. Pattern Recognition Letters 34, 1053 (2013).
    • (2013) Pattern Recognition Letters , vol.34 , pp. 1053
    • Li, X.Z.1    Williams, S.2    Bottema, M.J.3
  • 186
    • 84887356037 scopus 로고    scopus 로고
    • Texture and region dependent breast cancer risk assessment from screening mammograms
    • X. Z. Li, S. Williams, and M. J. Bottema, Texture and region dependent breast cancer risk assessment from screening mammograms. Pattern Recognition Letters 36, 117 (2014).
    • (2014) Pattern Recognition Letters , vol.36 , pp. 117
    • Li, X.Z.1    Williams, S.2    Bottema, M.J.3
  • 188
    • 34247586627 scopus 로고    scopus 로고
    • Two graph theory based methods for identifying the pectoral muscle in mammograms
    • F. Ma, M. Bajger, J. P. Slavotinek, and M. J. Bottema, Two graph theory based methods for identifying the pectoral muscle in mammograms, Pattern Recognition 40, 2592 (2007).
    • (2007) Pattern Recognition , vol.40 , pp. 2592
    • Ma, F.1    Bajger, M.2    Slavotinek, J.P.3    Bottema, M.J.4
  • 189
    • 77951860041 scopus 로고    scopus 로고
    • Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model
    • L. Wang, M.-L. Zhu, L.-P. Deng, and X. Yuan, Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model. J. Zhejiang Univ. Sci. C 11, 111 (2010).
    • (2010) J. Zhejiang Univ. Sci. C , vol.11 , pp. 111
    • Wang, L.1    Zhu, M.-L.2    Deng, L.-P.3    Yuan, X.4
  • 190
    • 84865684717 scopus 로고    scopus 로고
    • A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis
    • C.-C. Liu, C.-Y. Tsai, J. Liu, C.-Y. Yu, and S.-S. Yu, A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis. Computers and Mathematics with Applications 64, 1100 (2012).
    • (2012) Computers and Mathematics with Applications , vol.64 , pp. 1100
    • Liu, C.-C.1    Tsai, C.-Y.2    Liu, J.3    Yu, C.-Y.4    Yu, S.-S.5
  • 192
    • 77957554809 scopus 로고    scopus 로고
    • Computer-aided detection of architectural distortion in prior mammograms of interval cancer
    • R. M. Rangayyan, S. Banik, and J. E. Desautels, Computer-aided detection of architectural distortion in prior mammograms of interval cancer. J. Digital Imaging 23, 611 (2010).
    • (2010) J. Digital Imaging , vol.23 , pp. 611
    • Rangayyan, R.M.1    Banik, S.2    Desautels, J.E.3
  • 196
    • 84931097846 scopus 로고    scopus 로고
    • Application of contour models for the detection of cancer tumors in breast tissue
    • H. S. Sheshadri and A. Kandaswamy, Application of contour models for the detection of cancer tumors in breast tissue. Internet J. Medical Simulation 1 (2004).
    • (2004) Internet J. Medical Simulation , vol.1
    • Sheshadri, H.S.1    Kandaswamy, A.2
  • 197
    • 41549151243 scopus 로고    scopus 로고
    • Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis
    • R. M. Rangayyan, S. Prajna, F. J. Ayres, and J. E. Desautels, Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis. Int. J. Computer Assisted Radiology and Surgery 2, 347 (2008).
    • (2008) Int. J. Computer Assisted Radiology and Surgery , vol.2 , pp. 347
    • Rangayyan, R.M.1    Prajna, S.2    Ayres, F.J.3    Desautels, J.E.4
  • 198
    • 48749106814 scopus 로고    scopus 로고
    • Textural classification of mammographic parenchymal patterns with the SONNET selforganising neural network
    • D. Howard, S. C. Roberts, C. Ryan, and A. Brezulianu, Textural classification of mammographic parenchymal patterns with the SONNET selforganising neural network. J. Biomedicine and Biotechnology 1 (2008).
    • (2008) J. Biomedicine and Biotechnology , pp. 1
    • Howard, D.1    Roberts, S.C.2    Ryan, C.3    Brezulianu, A.4
  • 202
    • 77549085806 scopus 로고    scopus 로고
    • A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients
    • D. Soria, J. M. Garibaldi, and F. Ambrogi, A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. Computers in Biology and Medicine 40, 318 (2010).
    • (2010) Computers in Biology and Medicine , vol.40 , pp. 318
    • Soria, D.1    Garibaldi, J.M.2    Ambrogi, F.3
  • 203
    • 77949269627 scopus 로고    scopus 로고
    • Application of k- and fuzzy c-means for color segmentation of thermal infrared breast images
    • M. EtehadTavakol, S. Sadri, and E. Y. K. Ng, Application of k- and fuzzy c-means for color segmentation of thermal infrared breast images. J. Med. Sys. 34, 35 (2010).
    • (2010) J. Med. Sys. , vol.34 , pp. 35
    • EtehadTavakol, M.1    Sadri, S.2    Ng, E.Y.K.3
  • 205
    • 0032587727 scopus 로고    scopus 로고
    • Statistical textural for detection of microcalcifications in digitized mammograms
    • J. K. Kim and H. W. Park, Statistical textural for detection of microcalcifications in digitized mammograms. IEEE Transactions on Medical Imaging 18, 231 (1999).
    • (1999) IEEE Transactions on Medical Imaging , vol.18 , pp. 231
    • Kim, J.K.1    Park, H.W.2
  • 206
    • 0032050535 scopus 로고    scopus 로고
    • Detection of clustered microcalcifications on mammograms using surrounding region dependence method and artificial neural network
    • J. K. Kim, J. M. Park, K. S. Song, and H. W. Park, Detection of clustered microcalcifications on mammograms using surrounding region dependence method and artificial neural network. J. VLSI Signal Processing 18, 251 (1998).
    • (1998) J. VLSI Signal Processing , vol.18 , pp. 251
    • Kim, J.K.1    Park, J.M.2    Song, K.S.3    Park, H.W.4
  • 207
  • 208
    • 84877720358 scopus 로고    scopus 로고
    • Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT)
    • P. Gorgel, A. Sertbas, and O. N. Ucan, Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT). Computers in Biology and Medicine 43, 765 (2013).
    • (2013) Computers in Biology and Medicine , vol.43 , pp. 765
    • Gorgel, P.1    Sertbas, A.2    Ucan, O.N.3
  • 209
    • 84155181072 scopus 로고    scopus 로고
    • ANN versus SVM: Which one performs better in classification of MCCs in mammogram imaging?
    • J. Ren, ANN versus SVM: Which one performs better in classification of MCCs in mammogram imaging?. Knowledge-Based Systems 26, 144 (2011).
    • (2011) Knowledge-Based Systems , vol.26 , pp. 144
    • Ren, J.1
  • 210
    • 56349123043 scopus 로고    scopus 로고
    • An expert system for detection of breast cancer based on association rules and neural network
    • M. Karabatak and M. C. Ince, An expert system for detection of breast cancer based on association rules and neural network. Expert Systems with Applications 36, 3465 (2009).
    • (2009) Expert Systems with Applications , vol.36 , pp. 3465
    • Karabatak, M.1    Ince, M.C.2
  • 212
    • 33744962383 scopus 로고    scopus 로고
    • Random sampling for subspace face recognition
    • X. Wang and X. Tang, Random sampling for subspace face recognition. International J. Computer Vision 70, 91 (2006).
    • (2006) International J. Computer Vision , vol.70 , pp. 91
    • Wang, X.1    Tang, X.2


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