Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement

[1]  Anant Madabhushi,et al.  Textural Kinetics: A Novel Dynamic Contrast-Enhanced (DCE)-MRI Feature for Breast Lesion Classification , 2011, Journal of Digital Imaging.

[2]  Mahesh B. Nagarajan,et al.  Performance of topological texture features to classify fibrotic interstitial lung disease patterns. , 2011, Medical physics.

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

[4]  Thomas Bülow,et al.  Heterogeneity of kinetic curve parameters as indicator for the malignancy of breast lesions in DCE MRI , 2010, Medical Imaging.

[5]  Hon J. Yu,et al.  Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. , 2008, Academic radiology.

[6]  M. Reiser,et al.  Automated classification of normal and pathologic pulmonary tissue by topological texture features extracted from multi-detector CT in 3D , 2008, European Radiology.

[7]  M. Reiser,et al.  Classification of Small Contrast Enhancing Breast Lesions in Dynamic Magnetic Resonance Imaging Using a Combination of Morphological Criteria and Dynamic Analysis Based on Unsupervised Vector-Quantization , 2008, Investigative radiology.

[8]  M. Giger,et al.  Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images , 2007, Magnetic resonance in medicine.

[9]  Dinggang Shen,et al.  STEP: SPATIAL-TEMPORAL ENHANCEMENT PATTERN, FOR MR-BASED BREAST TUMOR DIAGNOSIS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[10]  M. Reiser,et al.  Differentiation between post-menopausal women with and without hip fractures: enhanced evaluation of clinical DXA by topological analysis of the mineral distribution in the scan images , 2007, Osteoporosis International.

[11]  Lina Arbash Meinel,et al.  Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer‐aided diagnosis (CAD) system , 2007, Journal of magnetic resonance imaging : JMRI.

[12]  M. Reiser,et al.  Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions? , 2006, European Radiology.

[13]  Joachim Boettcher,et al.  Further Signs in the Evaluation of Magnetic Resonance Mammography: A Retrospective Study , 2005, Investigative radiology.

[14]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[15]  M. Giger,et al.  Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. , 2004, Medical physics.

[16]  B. Szabó,et al.  Dynamic MR imaging of the breast: Analysis of kinetic and morphologic diagnostic criteria , 2003, Acta radiologica.

[17]  L. Turnbull,et al.  Textural analysis of contrast‐enhanced MR images of the breast , 2003, Magnetic resonance in medicine.

[18]  L. Liberman,et al.  Breast imaging reporting and data system (BI-RADS). , 2002, Radiologic clinics of North America.

[19]  E. Grabbe,et al.  Classification of hypervascularized lesions in CE MR imaging of the breast , 2002, European Radiology.

[20]  M K Markey,et al.  Application of the mutual information criterion for feature selection in computer-aided diagnosis. , 2001, Medical physics.

[21]  Kristel Michielsen,et al.  Integral-geometry morphological image analysis , 2001 .

[22]  W. Kaiser,et al.  Development, standardization, and testing of a lexicon for reporting contrast‐enhanced breast magnetic resonance imaging studies , 2001, Journal of magnetic resonance imaging : JMRI.

[23]  U. Bick,et al.  Differentiation between benign and malignant findings on MR-mammography: usefulness of morphological criteria , 2001, European Radiology.

[24]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[25]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[26]  C. Kuhl,et al.  Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? , 1999, Radiology.

[27]  M L Giger,et al.  Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. , 1998, Medical physics.

[28]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[29]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[30]  S. P. Wright,et al.  Adjusted P-values for simultaneous inference , 1992 .

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

[32]  Axel Wismüller,et al.  Classification of Small Lesions in Breast MRI: Evaluating The Role of Dynamically Extracted Texture Features Through Feature Selection. , 2013, Journal of medical and biological engineering.

[33]  Anke Meyer-Bäse,et al.  Segmentation and classification of dynamic breast magnetic resonance image data , 2006, J. Electronic Imaging.

[34]  M. Giger,et al.  A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. , 2006, Academic radiology.

[35]  Maryellen L. Giger,et al.  A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1 , 2006 .

[36]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[37]  David G. Stork,et al.  Pattern Classification , 1973 .

[38]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .