ABUTABLEB, A. S. (1989) Automatic thresholding of gray-level pictures using two-dimensional entropy. Computer Vision, Graphics, and Image Processing, 47, 22 - 32.
ADAMS, R. & BISCHOF, L. (1994) Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 641-647.
AHEARN, T. S., STAFF, R. T., REDPATH, T. W. & SEMPLE, S. I. K. (2005) The use of the Levenberg�Marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data. Physics in Medicine and Biology, 50, N85-N92.
AMERICAN COLLEGE OF RADIOLOGY (2006) Breast imaging reporting and data system (BI-RADS). www.arc.org, American College of Radiology.
ARBACH, L., STOLPEN, A. & REINHARDT, J. M. (2004) Classification of breast MRI lesions using a backpropagation neural network (BNN). Biomedical Imaging: Macro to Nano, 2004. IEEE International Symposium on, 1, 253-256.
ARIFIN, A. Z. & ASANO, A. (2006) Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recognition Letters, 27, 1515-1521.
AUSTRALIAN INSTITUTE OF HEALTH AND WELFARE (2007) Cancer in Australia 2006. Australian Institute of Health and Welfare AIHW and Australian Association of Cancer Registries AACR: Canberra.
AUSTRALIAN INSTITUTE OF HEALTH AND WELFARE (2009) Breast cancer in Australia: An Overview, 2009. Canberra, Australian Institute of Health and Welfare and National Breast and Ovarian Cancer Centre, Australian Institute of Health and Welfare.
AWATE, S. & WHITAKER, R. (2005a) Higher-Order Image Statistics for Unsupervised, Information-Theoretic, Adaptive, Image Filtering. Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society International Conference on. San Diego, IEEE Computer Society; 1999.
AWATE, S. P. & WHITAKER, R. T. (2005b) Nonparametric Neighborhood Statistics for MRI Denoising. Lecture Notes in Computer Science, 3565/2005, 12.
AWATE, S. P. & WHITAKER, R. T. (2006) Unsupervised, information-theoretic, adaptive image filtering for image restoration. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28, 364-376.
AWATE, S. P. & WHITAKER, R. T. (2007) Feature-Preserving MRI Denoising: A Nonparametric Empirical Bayes Approach. Medical Imaging, IEEE Transactions on, 26, 1242-1255.
BAO, P. & ZHANG, L. (2003) Noise reduction for magnetic resonance images via adaptive multiscale products thresholding. IEEE Transactions on Medical Imaging, 22, 1089-1099.
BEHRENS, S., LAUE, H., ALTHAUS, M., BOEHLER, T., KUEMMERLEN, B., HAHN, H. K. & PEITGEN, H. O. (2007) Computer assistance for MR based diagnosis of breast cancer: Present and future challenges. Computerized Medical Imaging and Graphics, 31, 236-247.
BLACK, M. J., SAPIRO, G., MARIMONT, D. H. & HEEGER, D. (1998) Robust anisotropic diffusion. Image Processing, IEEE Transactions on, 7, 421-432.
BRIX, G., SEMMLER, W., PORT, R., SCHAD, L. R., LAYER, G. & LORENZ, W. J. (1991) Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. Journal of Computer Assisted Tomography, 15, 621-8.
BUADES, A., COLL, B. & MOREL, J. M. (2004) On image denoising methods. CMLA Preprint, 5.
BUADES, A., COLL, B. & MOREL, J. M. (2005a) A non-local algorithm for image denoising. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
BUADES, A., COLL, B. & MOREL, J. M. (2005b) A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling & Simulation, 4, 490.
BUADES, A., COLL, B. & MOREL, J. M. (2008) Nonlocal Image and Movie Denoising. International Journal of Computer Vision, 76, 123-139.
BUCKLEY, D. L., KERSLAKE, R. W., BLACKBAND, S. J. & HORSMAN, A. (1994) Quantitative analysis of multi-slice Gd-DTPA enhanced dynamic MR images using an automated simplex minimization procedure. Magn Reson Med, 32, 646-51.
CHEN, W., GIGER, M. L. & BICK, U. (2006a) A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images. Academic Radiology, 13, 63-72.
CHEN, W., GIGER, M. L., BICK, U. & NEWSTEAD, G. M. (2006b) Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Medical Physics, 33, 2878-2887.
CHEN, W., GIGER, M. L., LAN, L. & BICK, U. (2004) Computerized interpretation of breast MRI: Investigation of enhancement-variance dynamics. Medical Physics, 31, 1076-1082.
CHEN, X., MICHAEL, B., JONATHAN LOK-CHUEN, L. & NIALL, M. (2005) Simultaneous Segmentation and Registration of Contrast-Enhanced Breast MRI. Information Processing in Medical Imaging. Springer Berlin / Heidelberg.
CHENG, H. D., CHEN, Y. H. & JIANG, X. H. (2000) Thresholding using two-dimensional histogram and fuzzy entropy principle. IEEE Transactions on Image Processing, 9, 732-735.
CLAUS, E. B., RISCH, N., THOMPSON, W. D. & CARTER, D. (1993) Relationship between breast histopathology and family history of breast cancer. Cancer, 71, 147 - 153.
COIFMAN, R. R. & WICKERHAUSER, M. V. (1994) Adapted waveform analysis as a tool for modeling, feature extraction, and denoising. Optical Engineering, 33, 2170-2174.
COIFMAN, R. R., WICKERHAUSER, M. V. & WOOG, L. (1997) Adaptive design toolkit software. New Haven: Fast Mathematical Algorithms and Software Corporation.
COLLINS, D. J. & PADHANI, A. R. (2004) Dynamic magnetic resonance imaging of tumor perfusion. Engineering in Medicine and Biology Magazine, IEEE, 23, 65-83.
COMON, P. (1994) Independent component analysis, a new concept. Signal Processing, 36, 287-314.
CORTES, C. & VAPNIK, V. (1995) Support-vector networks. Machine learning, 20, 273-297.
COUPE, P., YGER, P., PRIMA, S., HELLIER, P., KERVRANN, C. & BARILLOT, C. (2008) An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images. Medical Imaging, IEEE Transactions on, 27, 425-441.
DANGETI, S. V. (2002) Denoising Techniques-A Comparison. Electrical and Computer Engineering. Visakhapatnam, India, Andhra University College of Engineering.
DEGANI, H., GUSIS, V., WEINSTEIN, D., FIELDS, S. & STRANO, S. (1997) Mapping pathophysiological features of breast tumors by MRI at high spatial resolution. Nat Med, 3, 780-782.
DONOHO, D. L. & JOHNSTONE, I. M. (1994) Ideal Spatial Adaptation via Wavelet Shrinkage. Biometrika, 81, 425-455.
FAN, X., MEDVED, M., RIVER, J. N., ZAMORA, M., COROT, C., ROBERT, P., BOURRINET, P., LIPTON, M., CULP, R. M. & KARCZMAR, G. S. (2004) New model for analysis of dynamic contrast-enhanced MRI data distinguishes metastatic from nonmetastatic transplanted rodent prostate tumors. Magnetic Resonance in Medicine, 51, 487-494.
FISCHER, U. & BRINCK, U. (2004) Practical MR Mammography, Stuttgart, Germany, Thieme Publishing Group.
FURMAN-HARAN, E. & DEGANI, H. (2002) Parametric analysis of breast MRI. Journal of Computer Assisted Tomography, 26, 376-86.
GAL, Y., MEHNERT, A., BRADLEY, A., KENNEDY, D. & CROZIER, S. (2009a) Feature and Classifier Selection for Automatic Classification of Lesions in Dynamic Contrast-Enhanced MRI of the breast. Proceedings Digital Image Computing: Techniques and Applications (DICTA). Melbourne, Australia.
GAL, Y., MEHNERT, A., BRADLEY, A., MCMAHON, K. & CROZIER, S. (2007a) Automatic Segmentation of Enhancing Breast Tissue in Dynamic Contrast-Enhanced MR Images. IN J. BOTTEMA, M., MAEDER, A., REDDING, N. & VAN DEN HENGEL, A. (Eds.) Proceedings Digital Image Computing: Techniques and Applications (DICTA). Adelaide, Australia.
GAL, Y., MEHNERT, A., BRADLEY, A., MCMAHON, K. & CROZIER, S. (2007b) An evaluation of four parametric models of contrast enhancement for dynamic magnetic resonance imaging of the breast. IEEE Engineering in Medicine and Biology Society (EMBC). Lyon, France.
GAL, Y., MEHNERT, A., BRADLEY, A., MCMAHON, K. & CROZIER, S. (2009b) A Variation on Non-Local Means for the Denoising of Dynamic Contrast-Enhanced MR Images. Symposium on GPGPU Techniques for Medical Image Processing and Simulation. Brisbane, Australia, EMB.
GAL, Y., MEHNERT, A., BRADLEY, A., MCMAHON, K., KENNEDY, D. & CROZIER, S. (2008) A new denoising method for dynamic contrast-enhanced MRI. Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE. Vancouver, British Columbia, Canada.
GAL, Y., MEHNERT, A., BRADLEY, A., MCMAHON, K., KENNEDY, D. & CROZIER, S. (2010) Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Non-Local Means. IEEE Transactions on Medical Imaging, 29, 302-310.
GEMAN, S., BIENENSTOCK, E. & DOURSAT, R. (1992) Neural networks and the bias/variance dilemma. Neural computation, 4, 1-58.
GERIG, G., KUBLER, O., KIKINIS, R. & JOLESZ, F. A. (1992) Nonlinear anisotropic filtering of MRI data. Medical Imaging, IEEE Transactions on, 11, 221-232.
GILHUIJS, K. G., GIGER, M. L. & BICK, U. (1999) A method for computerized assessment of tumor extent in contrast-enhanced MR images of the breast. Computer-aided diagnosis in medical imaging. Philadelphia, PA, Elsevier Science B.V.
GONZALEZ & WOODS, R. E. (2002) Digital Image Processing, Prentice Hall.
GUDBJARTSSON, H. & PATZ, S. (1996) The Rician distribution of noisy MRI data (vol 34, pg 910, 1995). Magnetic Resonance in Medicine, 36, 332-332.
HAACKE, E., THOMPSON, M., BROWN, R. & VENKATESAN, R. (1999) Magnetic resonance imaging: physical principles and sequence design, Wiley New York.
HANLEY, J. A. & MCNEIL, B. (1982) The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve. Radiology, 143, 29-36.
HARALICK, R., DINSTEIN, I. & SHANMUGAM, K. (1973) Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 3, 610-621.
HAYTON, P., BRADY, M., TARASSENKO, L. & MOORE, N. (1997) Analysis of dynamic MR breast images using a model of contrast enhancement. Medical Image Analysis, 1, 207-24.
HAYTON, P. M. (1998) Analysis of contrast-enhanced breast MRI. Department of Engineering Science. Oxford, The University of Oxford, Oxford, UK.
HEIBERG, E. V., PERMAN, W. H., HERRMANN, V. M. & JANNEY, C. G. (1996) Dynamic sequential 3D gadolinium-enhanced MRI of the whole breast. Magnetic Resonance Imaging, 14, 337-348.
HITTMAIR, K., GOMISCEK, G., LANGENBERGER, K., RECHT, M., IMHOF, H. & KRAMER, J. (1994) Method For the Quantitative Assessment of Contrast Agent Uptake in Dynamic Contrast-Enhanced Mrt. Magnetic Resonance in Medicine, 31, 567-571.
JACKSON, A., BUCKLEY, D. L. & PARKER, G. J. M. (2005) Dynamic contrast-enhanced magnetic resonance imaging in oncology New York, Springer.
JACOBS, M. A., BARKER, P. B., BLUEMKE, D. A., MARANTO, C., ARNOLD, C., HERSKOVITS, E. H. & BHUJWALLA, Z. (2003) Benign and Malignant Breast Lesions: Diagnosis with Multiparametric MR Imaging. Radiology, 229, 225-232.
JAIN, A. K., DUIN, R. P. W. & MAO, J. (2000) Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 22, 4�37.
JANSEN, S. A., FAN, X., KARCZMAR, G. S., ABE, H., SCHMIDT, R. A. & NEWSTEAD, G. M. (2008) Differentiation between benign and malignant breast lesions detected by bilateral dynamic contrast-enhanced MRI: A sensitivity and specificity study. Magnetic Resonance in Medicine, 59, 747-754.
JIANG, W., BAKER, M. L., WU, Q., BAJAJ, C. & CHIU, W. (2003) Applications of a bilateral denoising filter in biological electron microscopy. Journal of Structural Biology, 144, 114-122.
KASS, M., WITKIN, A. & TERZOPOULOS, D. (1988) Snakes: Active contour models. International Journal of Computer Vision, 1, 321-331.
KEREN, D., OSADCHY, M. & GOTSMAN, C. (2000) Anti-Faces for Detection. Proceedings of the 6th European Conference on Computer Vision-Part I, 134-148.
KEREN, D., OSADCHY, M. & GOTSMAN, C. (2001) Antifaces: a novel, fast method for image detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 747-761.
KERVRANN, C., BOULANGER, J. & COUPE, P. (2007) Bayesian Non-local Means Filter, Image Redundancy and Adaptive Dictionaries for Noise Removal. LECTURE NOTES IN COMPUTER SCIENCE, 4485, 520-532.
KOHLER, T. & LORENZ, D. (2005) A comparison of denoising methods for one dimensional time series. Bremen, Germany, University of Bremen.
KUHL, C. K., MIELCARECK, P., KLASCHIK, S., LEUTNER, C., WARDELMANN, E., GIESEKE, J. & SCHILD, H. H. (1999) Dynamic Breast MR Imaging: Are Signal Intensity Time Course Data Useful for Differential Diagnosis of Enhancing Lesions? Radiology, 211, 101-110.
KUPINSKI, M. A. & GIGER, M. L. (1998) Automated Seeded Lesion Segmentation on Digital Mammograms. IEEE TRANSACTIONS ON MEDICAL IMAGING, 17, 510-517.
LANDINI, G. & RIPPIN, J. W. (1996) How Important is Tumor Shape? Quantification of the Epithelial�Connective Tissue Interface in Oral Lesions Using Local COonnected Fractal Dimension Analysis. The Journal of Pathology, 179, 210-217.
LARSSON, H. B., STUBGAARD, M., FREDERIKSEN, J. L., JENSEN, M., HENRIKSEN, O. & PAULSON, O. B. (1990) Quantitation of blood-brain barrier defect by magnetic resonance imaging and gadolinium-DTPA in patients with multiple sclerosis and brain tumors. Magnetic Resonance Medicine, 16, 117-31.
LEE, S. H., KIM, J. H., KIM, K. G., PARK, J. S., PARK, S. J. & MOON, W. K. (2007) Optimal Clustering of Kinetic Patterns on Malignant Breast Lesions: Comparison between K-means Clustering and Three-time-points Method in Dynamic Contrast-enhanced MRI. Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE.
LEGGETT, J. (2004) Multi-layer Gradient Coil Design. School of Physics & Astronomy, Faculty of Science. Nottingham, UK, University of Nottingham.
LEVIN, A., ZOMET, A. & WEISS, Y. (2003) Learning how to inpaint from global image statistics. Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on.
LI, H. D., KALLERGI, M., CLARKE, L. P., JAIN, V. K. & CLARK, R. A. (1995) Markov random field for tumor detection in digital mammography. Medical Imaging, IEEE Transactions on, 14, 565-576.
LIU, P. F., DEBATIN, J. F., CADUFF, R. F., KACL, G., GARZOLI, E. & KRESTIN, G. P. (1998) Improved diagnostic accuracy in dynamic contrast enhanced MRI of the breast by combined quantitative and qualitative analysis. British Journal of Radiology, 71, 501-509.
LIU, Y. L., WANG, J., CHEN, X., GUO, Y. W. & PENG, Q. S. (2008) A Robust and Fast Non-Local Means Algorithm for Image Denoising. Journal of Computer Science and Technology, 23, 270-279.
LUCAS-QUESADA, F. A., SINHA, U. & SINHA, S. (2005) Segmentation strategies for breast tumors from dynamic MR images. Journal of Magnetic Resonance Imaging, 6, 753 - 763.
LYSAKER, M., LUNDERVOLD, A. & XUE-CHENG, T. (2003) Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. Image Processing, IEEE Transactions on, 12, 1579-1590.
MACOVSKI, A. (1996) Noise in MRI. Magn Reson Med, 36, 494-7.
MAHMOUDI, M. & SAPIRO, G. (2005) Fast image and video denoising via nonlocal means of similar neighborhoods. Signal Processing Letters, IEEE, 12, 839-842.
MALLADI, R. & SETHIAN, J. A. (1995) Level set methods for curvature flow, image enhancement, and shape recovery in medical images. Proc. of Conf. on Visualization and Mathematics, June.
MALLADI, R. & SETHIAN, J. A. (1996) Level set and fast marching methods in image processing and computer vision. Image Processing, 1996. Proceedings.
MALLAT, S. & ZHONG, S. (1992) Characterization of signals from multiscale edges. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 14, 710-732.
MANJ�N, J. V., CARBONELL-CABALLERO, J., LULL, J. J., GARC�A-MART�, G., MART�-BONMAT�, L. & ROBLES, M. (2008) MRI denoising using Non-Local Means. Medical Image Analysis, 12, 514-523.
MANJON, J. V., ROBLES, M. & THACKER, N. A. (2007) Multispectral MRI De-noising Using Non-Local Means. Medical Image Understanding and Analysis (MIUA).
MARQUARDT, D. W. (1963) An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11, 431-441.
MARRON, J. S. & TSYBAKOV, A. B. (1995) Visual Error Criteria for Qualitative Smoothing. Journal of the American Statistical Association, 90, 499-507.
MARTEL, A. L. (2006) A Fast Method of Generating Pharmacokinetic Maps from Dynamic Contrast-Enhanced Images of the Breast. MICCAI.
MATHWORKS (2007) MATLAB. 7.4.0.287 (R2007a) ed., The MathWorks inc.
MEHNERT, A., BENGTSSON, E., MCMAHON, K., KENNEDY, D., WILSON, S. & CROZIER, S. (2005) Visualisation of the pattern of contrast enhancement in dynamic breast MRI. Proceedings WDIC2005, APRS Workshop on Digital Image Computing Griffith University, Southbank, Brisbane, Australia, The University of Queensland.
MEHNERT, A. & JACKWAY, P. (1997) An improved seeded region growing algorithm. Pattern Recognition Letters, 18, 1065-1071.
MOORE, B. (1981) Principal component analysis in linear systems: Controllability, observability, and model reduction. Automatic Control, IEEE Transactions on, 26, 17-32.
MORRIS, E. A. & LIBERMAN, L. (2005) Breast MRI - diagnosis and intervention, New York, Springer.
MURASE, K., YAMAZAKI, Y., SHINOHARA, M., KAWAKAMI, K., KIKUCHI, K., MIKI, H., MOCHIZUKI, T. & IKEZOE, J. (2001) An anisotropic diffusion method for denoising dynamic susceptibility contrast-enhanced magnetic resonance images. Physics in Medicine and Biology, 46, 2713-2723.
NATTKEMPER, T. W., ARNRICH, B., LICHTE, O., TIMM, W., DEGENHARD, A., POINTON, L., HAYES, C. & LEACH, M. O. (2005) Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. Artificial Intelligence in Medicine, 34, 129-139.
NELDER, J. A. & MEAD, R. (1965) A simplex method for function minimization. Computer Journal, 7, 308-313.
NOWAK, R. D. (1999) Wavelet-based Rician noise removal for magnetic resonance imaging. Image Processing, IEEE Transactions on, 8, 1408-1419.
OREL, S. & SCHNALL, M. (2001) MR Imaging of the Breast for the Detection, Diagnosis, and Staging of Breast Cancer1. Radiology, 220, 13.
OTSU, N. (1979) A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on System, Man and Cybernetics, SMC-9, 62-66.
PAL, N. R. & PAL, S. K. (1993) A review on image segmentation techniques. Pattern Recognition, 26, 1277-1294.
PAN, Q., ZHANG, L., DAI, G. & ZHANG, H. (1999) Two denoising methods by wavelet transform. Signal Processing, IEEE Transactions on, 47, 3401-3406.
PERONA, P. & MALIK, J. (1990) Scale-space and edge detection using anisotropic diffusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 12, 629-639.
PETRICK, N., CHAN, H. P., SAHINER, B. & HELVIE, M. A. (1999) Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. Medical Physics, 26, 1642.
PIZURICA, A., PHILIPS, W., LEMAHIEU, I. & ACHEROY, M. (2003) A versatile wavelet domain noise filtration technique for medical imaging. Medical Imaging, IEEE Transactions on, 22, 323-331.
POHLE, R. & TOENNIES, K. D. (2001) Segmentation of medical images using adaptive region growing. Proc. SPIE Medical Imaging, 4322, 1337�1346.
POOLE, M. (2007) Improved Equipment and Techniques for Dynamic Shimming in High Field MRI. Nottingham, University of Nottingham.
REVOL, C. & JOURLIN, M. (1997) A new minimum variance region growing algorithm for image segmentation. Pattern Recognition Letters, 18, 249-258.
RICE, S. (1944) Mathematical analysis of random noise. Bell Systems Technology Journal, 23, 282-332.
RIMEY, R. D. & COHEN, F. S. (1988) A maximum-likelihood approach to segmenting range data. Robotics and Automation, IEEE Journal of [see also IEEE Transactions on Robotics and Automation], 4, 277-286.
RODENACKER, K. & BENGTSSON, E. (2003) A feature set for cytometry on digitized microscopic images. Analytical Cellular Pathology, 25, 1-36.
ROSSET, A., SPADOLA, L. & RATIB, O. (2004) OsiriX: an open-source software for navigating in multidimensional DICOM images. Journal of Digital Imaging, 17, 205-216.
SETHIAN, J. A. (1996) A fast marching level set method for monotonically advancing fronts.
SETHIAN, J. A. (1999) Level Set Methods and Fast Marching Methods. Evolving interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Univ. Press, Cambridge.
SEZGIN, M. & SANKUR, B. (2004) Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13, 146-168.
SINHA, S., LUCAS-QUESADA, F. A., DEBRUHL, N. D., SAYRE, J., FARRIA, D., GORCZYCA, D. P. & BASSETT, L. W. (1997) Multifeature analysis of Gd-enhanced MR images of breast lesions. Journal of Magnetic Resonance Imaging, 7, 1016-26.
SNYDER, W. E. & QI, H. (2004) Machine Vision, Cambridge, UK, Cambridge University Press.
SYKULSKI, J. K., ROTARU, M., SABENE, M. & SANTILLI, M. (1998) Comparison of optimization techniques for electromagnetic applications. COMPEL � The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 17, 171-176.
SZABO, B. K., ASPELIN, P., KRISTOFFERSEN WIBERG, M. & BON�, B. (2003) Dynamic MR Imaging of the breast: Analysis of kinetic and morphologic diagnostic criteria. Acta Radiologica, 44, 379-386.
TISDALL, D. & ATKINS, M. S. (2005) MRI denoising via phase error estimation. IN FITZPATRICK, M. J. (Ed.) Procedings of SPIE. San Diego, CA, SPIE, Bellibham, WA.
TOFTS, P. S., BERKOWITZ, B. & SCHNALL, M. D. (1995) Quantitative-analysis of dynamic Gd-DTPA enhancement in breast-tumors using a permeability model. Magnetic Resonance in Medicine, 33, 564-568.
TOFTS, P. S. & BERKOWITZ, B. A. (1994) Measurement of capillary permeability from the Gd enhancement curve: A comparison of bolus and constant infusion injection methods. Magnetic Resonance Imaging, 12, 81-91.
TOFTS, P. S. & KERMODE, A. G. (1991) Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magnetic Resonance Medicine, 17, 357-67.
TOMASI, C. & MANDUCHI, R. (1998) Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision. Bombay, India.
TORHEIM, G., GODTLIEBSEN, F., AXELSON, D., KVISTAD, K. A., HARALDSETH, A. & RINCK, P. A. (2001) Feature extraction and classification of dynamic contrast-enhancedT2*-weighted breast image data. IEEE Transactions on Medical Imaging, 20, 1293-1301.
TURK, M. A. & PENTLAND, A. P. (1991) Face recognition using eigenfaces. Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on.
VIDHOLM, E., MEHNERT, A., BENGTSSON, E., WILDERMOTH, M., MCMAHON, K., WILSON, S. & CROZIER, S. (2007) Hardware-accelerated volume visualisation of parametrically mapped dynamic breast MRI data. The 10th International Conference on Medical Image Computing and Computer Assisted Intervention Brisbane, Australia MICCAI.
WALKER, S. A., MILLER, D. & TANABE, J. (2006) Bilateral spatial filtering: Refining methods for localizing brain activation in the presence of parenchymal abnormalities. NeuroImage, 33, 564-569.
WARREN, R. & COULTHARD, A. (2002) Breast MRI in practice, London, UK, Martin Dunitz.
WARREN, R., POINTON, L., THOMPSON, D., HOFF, R., GILBERT, F., PADHANI, A., EASTON, D., LAKHANI, S. & LEACH, M. (2005) Reading Protocol for Dynamic Contrast-enhanced MR Images of the Breast: Sensitivity and Specificity Analysis1. Radiology, 236, 779.
WEBB, A. (2003) Statistical Pattern Recognition, Chichester, England, John Wiley & Sons Ltd.
WIEST-DAESSLE, N., PRIMA, S., COUPE, P., MORRISSEY, S. P. & BARILLOT, C. (2008) Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: Application to DT-MRI. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). New York.
WIEST-DAESSLE, N., PRIMA, S., COUPE, P., PATRICK MORRISSEY, S. & BARILLOT, C. (2007) Non-Local Means Variants for Denoising of Diffusion-Weighted and Diffusion Tensor MRI. IN AYACHE, N., OURSELIN, S. & MAEDER, A. (Eds.) Medical Image Computing and Computer-Assisted Intervention -- MICCAI. Brisbane, Australia, Springer.
WONG, A. K. C. & SAHOO, P. K. (1989) A gray-level threshold selection method based on maximum entropyprinciple. Systems, Man and Cybernetics, IEEE Transactions on, 19, 866-871.
WOOD, J. C. & JOHNSON, K. M. (1999) Wavelet packet denoising of magnetic resonance images: Importance of Rician noise at low SNR. Magnetic Resonance in Medicine, 41, 631-635.
XIE, J., HENG, P.-A., HO, S. S. M. & SHAH, M. (2006) Image Diffusion Using Saliency Bilateral Filter. Medical Image Computing and Computer-Assisted Intervention (MICCAI). Copenhagen, Denmark, Springer Berlin / Heidelberg.
XUAN, J., TULAY, A. & WANG, Y. (1995) Segmentation of Magnetic Resonance Brain Image: Integrating Region Growing and Edge Detection. Image Processing, 1995. Proceedings., International Conference on, 3, 544-547.