A new automatic mass detection method for breast cancer with false positive reduction

[1]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[2]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[3]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  S. Lai,et al.  On techniques for detecting circumscribed masses in mammograms. , 1989, IEEE transactions on medical imaging.

[5]  D. Chakraborty,et al.  Free-response methodology: alternate analysis and a new observer-performance experiment. , 1990, Radiology.

[6]  Martin D. Fox,et al.  Classifying mammographic lesions using computerized image analysis , 1993, IEEE Trans. Medical Imaging.

[7]  Rangaraj M. Rangayyan,et al.  Application of shape analysis to mammographic calcifications , 1994, IEEE Trans. Medical Imaging.

[8]  Vijay K. Jain,et al.  Markov random field for tumor detection in digital mammography , 1995, IEEE Trans. Medical Imaging.

[9]  M. Giger,et al.  Analysis of spiculation in the computerized classification of mammographic masses. , 1995, Medical physics.

[10]  Martin P. DeSimio,et al.  Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency , 1997, IEEE Transactions on Medical Imaging.

[11]  Maryellen L. Giger,et al.  Automated seeded lesion segmentation on digital mammograms , 1998, IEEE Transactions on Medical Imaging.

[12]  Nico Karssemeijer,et al.  Single and multiscale detection of masses in digital mammograms , 1999, IEEE Transactions on Medical Imaging.

[13]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[14]  Rangaraj M. Rangayyan,et al.  Gradient and texture analysis for the classification of mammographic masses , 2000, IEEE Transactions on Medical Imaging.

[15]  A. Chan,et al.  An artificial intelligent algorithm for tumor detection in screening mammogram , 2001, IEEE Transactions on Medical Imaging.

[16]  Berkman Sahiner,et al.  Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization , 2001, IEEE Transactions on Medical Imaging.

[17]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[18]  N. Karssemeijer,et al.  Segmentation of suspicious densities in digital mammograms. , 2001, Medical physics.

[19]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[20]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Rangaraj M. Rangayyan,et al.  Automatic identification of the pectoral muscle in mammograms , 2004, IEEE Transactions on Medical Imaging.

[22]  Stephen Kwek,et al.  Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.

[23]  N. Karssemeijer,et al.  A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. , 2004, Medical physics.

[24]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[25]  Yunmei Chen,et al.  Using Prior Shapes in Geometric Active Contours in a Variational Framework , 2002, International Journal of Computer Vision.

[26]  N. Szekely,et al.  A hybrid system for detecting masses in mammographic images , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[27]  Lisa M. Kinnard,et al.  Steepest changes of a probability-based cost function for delineation of mammographic masses: a validation study. , 2004, Medical physics.

[28]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[29]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Kenji Suzuki,et al.  A dual-stage method for lesion segmentation on digital mammograms. , 2007, Medical physics.

[31]  Georgia D. Tourassi,et al.  A Concentric Morphology Model for the Detection of Masses in Mammography , 2007, IEEE Transactions on Medical Imaging.

[32]  Martin Kom,et al.  Automated detection of masses in mammograms by local adaptive thresholding , 2007, Comput. Biol. Medicine.

[33]  Mariusz Bajger,et al.  Two graph theory based methods for identifying the pectoral muscle in mammograms , 2007, Pattern Recognit..

[34]  Xavier Lladó,et al.  False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns , 2007, MICCAI.

[35]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[36]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[37]  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..

[38]  Rangaraj M. Rangayyan,et al.  Polygonal Modeling of Contours of Breast Tumors With the Preservation of Spicules , 2008, IEEE Transactions on Biomedical Engineering.

[39]  Allen R. Tannenbaum,et al.  Localizing Region-Based Active Contours , 2008, IEEE Transactions on Image Processing.

[40]  Xavier Lladó,et al.  A textural approach for mass false positive reduction in mammography , 2009, Comput. Medical Imaging Graph..

[41]  Defeng Wang,et al.  Automatic detection of breast cancers in mammograms using structured support vector machines , 2009, Neurocomputing.

[42]  Yongyi Yang,et al.  Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances , 2009, IEEE Transactions on Information Technology in Biomedicine.

[43]  Julien Mille,et al.  Narrow band region-based active contours and surfaces for 2D and 3D segmentation , 2009, Comput. Vis. Image Underst..

[44]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[45]  Arnau Oliver,et al.  A review of automatic mass detection and segmentation in mammographic images , 2010, Medical Image Anal..

[46]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[47]  Mariusz Nieniewski,et al.  Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm , 2011, Journal of Digital Imaging.

[48]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[49]  Xiaoming Liu,et al.  Classification of Breast Mass in Mammography with an Improved Level Set Segmentation by Combining Morphological Features and Texture Features , 2011 .

[50]  Juan F. Ramirez-Villegas,et al.  Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions , 2012, Neurocomputing.

[51]  Jaime S. Cardoso,et al.  INbreast: toward a full-field digital mammographic database. , 2012, Academic radiology.

[52]  Xiaoming Liu,et al.  Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method , 2014, IEEE Systems Journal.

[53]  Ge Yu,et al.  Breast tumor detection in digital mammography based on extreme learning machine , 2014, Neurocomputing.