Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection.
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[1] Heng-Da Cheng,et al. Approaches for automated detection and classification of masses in mammograms , 2006, Pattern Recognit..
[2] Berkman Sahiner,et al. Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices. , 2010, Medical physics.
[3] H P Chan,et al. Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. , 1999, Medical physics.
[4] Yong Man Ro,et al. Multiresolution Local Binary Pattern texture analysis for false positive reduction in computerized detection of breast masses on mammograms , 2012, Medical Imaging.
[5] Ludmila I. Kuncheva,et al. Combining Pattern Classifiers: Methods and Algorithms , 2004 .
[6] R A Schmidt,et al. Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study. , 2008, Medical physics.
[7] Yong Man Ro,et al. Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes , 2014, Physics in medicine and biology.
[8] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[9] I. Sechopoulos. A review of breast tomosynthesis. Part I. The image acquisition process. , 2013, Medical physics.
[10] Anil K. Jain,et al. Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[11] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[12] Mary M. Galloway,et al. Texture analysis using gray level run lengths , 1974 .
[13] R E Hendrick,et al. Benefit of mammography screening in women ages 40 to 49 Years. Current evidence from randomized controlled trials , 1995, Cancer.
[14] Berkman Sahiner,et al. Computer-aided detection system for breast masses on digital tomosynthesis mammograms: preliminary experience. , 2005, Radiology.
[15] Andrea Botti,et al. Contrast Detail Phantom Comparison on a Commercially Available Unit. Digital Breast Tomosynthesis (DBT) versus Full-Field Digital Mammography (FFDM) , 2011, Journal of Digital Imaging.
[16] T. Freer,et al. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. , 2001, Radiology.
[17] Arun Ross,et al. Score normalization in multimodal biometric systems , 2005, Pattern Recognit..
[18] Nico Karssemeijer,et al. Optimizing Case-Based Detection Performance in a Multiview CAD System for Mammography , 2011, IEEE Transactions on Medical Imaging.
[19] Bo Zhao,et al. Image artifacts in digital breast tomosynthesis: investigation of the effects of system geometry and reconstruction parameters using a linear system approach. , 2008, Medical physics.
[20] Sukhendu Das,et al. A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification , 2010, IETE Technical Review.
[21] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[22] Rangaraj M. Rangayyan,et al. Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer , 2006 .
[23] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[24] Lubomir M. Hadjiiski,et al. A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis. , 2006, Medical physics.
[25] Thomas Mertelmeier,et al. Optimizing filtered backprojection reconstruction for a breast tomosynthesis prototype device , 2006, SPIE Medical Imaging.
[26] Berkman Sahiner,et al. Computer-aided detection of masses in digital tomosynthesis mammography: comparison of three approaches. , 2008, Medical physics.
[27] Subhash C. Bagui,et al. Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.
[28] Azriel Rosenfeld,et al. A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.
[29] Konstantinos N. Plataniotis,et al. Ensemble-based discriminant learning with boosting for face recognition , 2006, IEEE Transactions on Neural Networks.
[30] Matti Pietikäinen,et al. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[31] J M Lesniak,et al. Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography , 2012, Physics in medicine and biology.
[32] Yong Man Ro,et al. Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms , 2012, Physics in medicine and biology.
[33] C P Lawinski,et al. A comparison of the accuracy of film-screen mammography, full-field digital mammography, and digital breast tomosynthesis. , 2012, Clinical radiology.
[34] Joseph Y. Lo,et al. Mutual information-based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis , 2011, J. Biomed. Informatics.
[35] Isabelle Bloch,et al. Detection of masses and architectural distortions in digital breast tomosynthesis images using fuzzy and a contrario approaches , 2014, Pattern Recognit..
[36] Berkman Sahiner,et al. Computer-aided detection of breast masses in digital breast tomosynthesis (DBT): improvement of false positive reduction by optimization of object segmentation , 2011, Medical Imaging.
[37] Byung-Woo Hong,et al. A Topographic Representation for Mammogram Segmentation , 2003, MICCAI.
[38] Yong Man Ro,et al. Mammographic enhancement with combining local statistical measures and sliding band filter for improved mass segmentation in mammograms , 2012, Medical Imaging.
[39] Vasile Palade,et al. Multi-Classifier Systems: Review and a roadmap for developers , 2006, Int. J. Hybrid Intell. Syst..
[40] Tao Wu,et al. A comparison of reconstruction algorithms for breast tomosynthesis. , 2004, Medical physics.
[41] Rangaraj M. Rangayyan,et al. Detection of breast masses in mammograms by density slicing and texture flow-field analysis , 2001, IEEE Transactions on Medical Imaging.
[42] A. Jemal,et al. Global Cancer Statistics , 2011 .
[43] Martin D. Fox,et al. Classifying mammographic lesions using computerized image analysis , 1993, IEEE Trans. Medical Imaging.
[44] Joseph Y. Lo,et al. Computer-aided detection of breast masses in tomosynthesis reconstructed volumes using information-theoretic similarity measures , 2008, SPIE Medical Imaging.
[45] M. Giger,et al. Computerized mass detection for digital breast tomosynthesis directly from the projection images. , 2006, Medical physics.
[46] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[47] D. Kopans,et al. Tomographic mammography using a limited number of low-dose cone-beam projection images. , 2003, Medical physics.
[48] Yong Man Ro,et al. Boosting Color Feature Selection for Color Face Recognition , 2011, IEEE Transactions on Image Processing.
[49] Nico Karssemeijer,et al. Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms. , 2013, Medical physics.
[50] Nico Karssemeijer,et al. The effect of feature selection methods on computer-aided detection of masses in mammograms , 2010, Physics in medicine and biology.
[51] Asoke K. Nandi,et al. Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection , 2008, Comput. Medical Imaging Graph..
[52] Carlos S. Pereira,et al. Detection of Lung Nodule Candidates in Chest Radiographs , 2007, IbPRIA.