Pattern Classification of Medical Images: Computer Aided Diagnosis

[1]  Nan Zhang,et al.  Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[2]  Simon R. Arridge,et al.  Calibration techniques and datatype extraction for time-resolved optical tomography , 2000 .

[3]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[4]  C. F. Beckmann,et al.  Tensorial extensions of independent component analysis for multisubject FMRI analysis , 2005, NeuroImage.

[5]  Peter Kovesi,et al.  Phase Preserving Denoising of Images , 1999 .

[6]  Brian Litt,et al.  Evolving a Bayesian classifier for ECG-based age classification in medical applications , 2008, Appl. Soft Comput..

[7]  Wael Badawy,et al.  Error-free computation of Daubechies wavelets for image compression applications , 2003 .

[8]  Yanchun Zhang,et al.  Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers , 2016, Comput. Methods Programs Biomed..

[9]  A. R. Summers,et al.  A wavelet-based method for improving signal-to-noise ratio and contrast in MR images. , 2000, Magnetic resonance imaging.

[10]  Andrew Zisserman,et al.  Efficient Additive Kernels via Explicit Feature Maps , 2012, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Ana Maria Mendonça,et al.  Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction , 2006, IEEE Transactions on Medical Imaging.

[12]  Xiaohong Joe Zhou,et al.  Anomalous diffusion expressed through fractional order differential operators in the Bloch-Torrey equation. , 2008, Journal of magnetic resonance.

[13]  Joachim Jonuscheit,et al.  Automatically Detecting Peaks in Terahertz Time-Domain Spectroscopy , 2010, 2010 20th International Conference on Pattern Recognition.

[14]  N. Michoux,et al.  Texture analysis on MR images helps predicting non-response to NAC in breast cancer , 2015, BMC Cancer.

[15]  Gerhard Tutz,et al.  Ridge estimation for multinomial logit models with symmetric side constraints , 2013, Comput. Stat..

[16]  J. Hyde,et al.  Characterization of continuously distributed cortical water diffusion rates with a stretched‐exponential model , 2003, Magnetic resonance in medicine.

[17]  Dinggang Shen,et al.  Deep Learning-Based Feature Representation for AD/MCI Classification , 2013, MICCAI.

[18]  Arvid Lundervold,et al.  ssessment of 3 D DCE-MRI of the kidneys using non-rigid image registration nd segmentation of voxel time courses rank , 2009 .

[19]  Peter Gibbs,et al.  Texture analysis in assessment and prediction of chemotherapy response in breast cancer , 2013, Journal of magnetic resonance imaging : JMRI.

[20]  Hans-Peter Kriegel,et al.  Protein function prediction via graph kernels , 2005, ISMB.

[21]  Jim Mintz,et al.  Human brain myelination and amyloid beta deposition in Alzheimer’s disease , 2007, Alzheimer's & Dementia.

[22]  Geoff J M Parker,et al.  Comparison of the performance of tracer kinetic model-driven registration for dynamic contrast enhanced MRI using different models of contrast enhancement. , 2006, Academic radiology.

[23]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

[24]  Djemel Ziou,et al.  Segmentation of Terahertz imaging using k-means clustering based on ranked set sampling , 2015, Expert Syst. Appl..

[25]  Cordelia Schmid,et al.  DeepMatching: Hierarchical Deformable Dense Matching , 2015, International Journal of Computer Vision.

[26]  Sonal S. Honale,et al.  A Review of Methods for Blood Vessel Segmentation in Retinal images , 2012 .

[27]  Xiaohong Joe Zhou,et al.  Studies of anomalous diffusion in the human brain using fractional order calculus , 2010, Magnetic resonance in medicine.

[28]  Gary E. Christensen,et al.  Consistent landmark and intensity-based image registration , 2002, IEEE Transactions on Medical Imaging.

[29]  Laurent Jacques,et al.  Diffeomorphic Registration of Images with Variable Contrast Enhancement , 2010, Int. J. Biomed. Imaging.

[30]  Wei Jin,et al.  Enhancing retinal image by the Contourlet transform , 2007, Pattern Recognit. Lett..

[31]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[32]  Lei Zhang,et al.  Retinal vessel extraction by matched filter with first-order derivative of Gaussian , 2010, Comput. Biol. Medicine.

[33]  Leo Dorst Tutorial: Structure-Preserving Representation of Euclidean Motions Through Conformal Geometric Algebra , 2010, Geometric Algebra Computing.

[34]  Ya-Ju Fan,et al.  On the Time Series $K$-Nearest Neighbor Classification of Abnormal Brain Activity , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[35]  Nassir Navab,et al.  Automatic alignment of renal DCE-MRI image series for improvement of quantitative tracer kinetic studies , 2008, SPIE Medical Imaging.

[36]  Arthur W. Toga,et al.  A wavelet-based statistical analysis of fMRI data , 2007, Neuroinformatics.

[37]  Y. Arai,et al.  A Fast DCT-SQ Scheme for Images , 1988 .

[38]  Jie-Jin Wang,et al.  Update: Systemic diseases and the cardiovascular system (V) Retinal Vascular Signs: A Window to the Heart? , 2017 .

[39]  Andrew Zisserman,et al.  Learning Local Feature Descriptors Using Convex Optimisation , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Christos Davatzikos,et al.  Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme , 2009, Magnetic resonance in medicine.

[41]  Yan Li,et al.  Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface , 2014, Comput. Methods Programs Biomed..

[42]  Erlend Hodneland,et al.  Normalized gradient fields for nonlinear motion correction of DCE-MRI time series , 2014, Comput. Medical Imaging Graph..

[43]  D. Selvathi,et al.  Brain MRI Slices Classification Using Least Squares Support Vector Machine , 2007 .

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

[45]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[46]  Laurent Risser,et al.  Motion Correction and Parameter Estimation in dceMRI Sequences: Application to Colorectal Cancer , 2011, MICCAI.

[47]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[48]  Yoshua Bengio,et al.  An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.

[49]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[50]  Henry Rusinek,et al.  Dynamic three-dimensional MR renography for the measurement of single kidney function: initial experience. , 2003, Radiology.

[51]  Scott Fields,et al.  Mapping pathophysiological features of breast tumors by MRI at high spatial resolution , 1997, Nature Medicine.

[52]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[53]  Marc'Aurelio Ranzato,et al.  Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[54]  Hisashi Kashima,et al.  Marginalized Kernels Between Labeled Graphs , 2003, ICML.

[55]  Nariane Chantler Support system. , 2003, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[56]  Hans-Peter Kriegel,et al.  Shortest-path kernels on graphs , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[57]  Djemel Ziou,et al.  Terahertz image segmentation based on K-harmonic-means clustering and statistical feature extraction modeling , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[58]  Robert D. Nowak,et al.  Wavelet-based Rician noise removal for magnetic resonance imaging , 1999, IEEE Trans. Image Process..

[59]  Olaf Sporns,et al.  Graph Theory Methods for the Analysis of Neural Connectivity Patterns , 2003 .

[60]  K. Fukunaga,et al.  Nonparametric Bayes error estimation using unclassified samples , 1972, CDC 1972.

[61]  Torsten Rohlfing,et al.  Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint , 2003, IEEE Transactions on Medical Imaging.

[62]  N. Michoux,et al.  Analysis of contrast-enhanced MR images to assess renal function , 2006, Magnetic Resonance Materials in Physics, Biology and Medicine.

[63]  S. Qian,et al.  Joint time-frequency analysis : methods and applications , 1996 .

[64]  Lior Rokach,et al.  Top-down induction of decision trees classifiers - a survey , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[65]  Danica Kragic,et al.  Cohomological learning of periodic motion , 2015, Applicable Algebra in Engineering, Communication and Computing.

[66]  B. Schölkopf,et al.  Modeling Human Motion Using Binary Latent Variables , 2007 .

[67]  Okan Esenturk,et al.  Applications of terahertz spectroscopy in biosystems. , 2007, Chemphyschem : a European journal of chemical physics and physical chemistry.

[68]  Kotagiri Ramamohanarao,et al.  Retinal artery-vein caliber grading using color fundus imaging , 2013, Comput. Methods Programs Biomed..

[69]  Kamaljeet Kaur,et al.  Classification of Abnormalities in Brain MRI Images using GLCM, PCA and SVM , 2012 .

[70]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[71]  Xuelong Li,et al.  Supervised Tensor Learning , 2005, ICDM.

[72]  Qiang He,et al.  Feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory. , 2010, Medical physics.

[73]  Anke Meyer-Bäse,et al.  Model-free visualization of suspicious lesions in breast MRI based on supervised and unsupervised learning , 2008, Eng. Appl. Artif. Intell..

[74]  Emanuele Trucco,et al.  Retinal vessel classification: Sorting arteries and veins , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[75]  Luciano da Fontoura Costa,et al.  Complex networks: The key to systems biology , 2008 .

[76]  Scott Kirkpatrick,et al.  Optimization by Simmulated Annealing , 1983, Sci..

[77]  Ioannis Delakis,et al.  Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI) , 2007, Physics in medicine and biology.

[78]  Jong Chul Ye,et al.  Wavelet Power Spectrum Estimation for High-resolution Terahertz Time-domain Spectroscopy , 2011 .

[79]  B. Efron Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation , 1983 .

[80]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[81]  Thomas M. Cover,et al.  Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..

[82]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[83]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[84]  Demetri Terzopoulos,et al.  Multilinear Analysis of Image Ensembles: TensorFaces , 2002, ECCV.

[85]  Aleksandra Pizurica,et al.  A versatile wavelet domain noise filtration technique for medical imaging , 2003, IEEE Transactions on Medical Imaging.

[86]  Aline Viol,et al.  Brain complex network analysis by means of resting state fMRI and graph analysis: Will it be helpful in clinical epilepsy? , 2014, Epilepsy & Behavior.

[87]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

[88]  Mihai Anitescu,et al.  The role of linear semi-infinite programming in signal-adapted QMF bank design , 1997, IEEE Trans. Signal Process..

[89]  Arvid Lundervold,et al.  A variational approach to image registration in dynamic contrast-enhanced MRI of the human kidney. , 2013, Magnetic resonance imaging.

[90]  Leo Dorst,et al.  The making of a geometric algebra package in Matlab , 1999 .

[91]  Hamid Krim,et al.  Discovering the Whole by the Coarse: A topological paradigm for data analysis , 2016, IEEE Signal Processing Magazine.

[92]  Guido Caldarelli,et al.  Scale-Free Networks , 2007 .

[93]  Mohan M. Trivedi,et al.  Segmentation of a high-resolution urban scene using texture operators , 1984, Comput. Vis. Graph. Image Process..

[94]  John C. Peterson,et al.  Ophthalmic Photography: Retinal Photography, Angiography, and Electronic Imaging, 2nd ed , 2002 .

[95]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[96]  Thomas Gärtner,et al.  Cyclic pattern kernels for predictive graph mining , 2004, KDD.

[97]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[98]  Karolin Baecker,et al.  Two Dimensional Signal And Image Processing , 2016 .

[99]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[100]  Roberto Kawakami Harrop Galvão,et al.  Wavelet-packet identification of dynamic systems in frequency subbands , 2006, Signal Process..

[101]  Alan C. Evans,et al.  Searching scale space for activation in PET images , 1996, Human brain mapping.

[102]  Saeed Kermani,et al.  A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina , 2013, Journal of medical signals and sensors.

[103]  Jun-ichi Nishizawa,et al.  Quantitative evaluation of mefenamic acid polymorphs by terahertz-chemometrics. , 2010, Journal of pharmaceutical sciences.

[104]  Yanchun Zhang,et al.  Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising , 2017, Comput. Vis. Image Underst..

[105]  P. Bankhead,et al.  Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement , 2012, PloS one.

[106]  Rachid Deriche,et al.  Using Canny's criteria to derive a recursively implemented optimal edge detector , 1987, International Journal of Computer Vision.

[107]  Stephen Hammond,et al.  Ophthalmoscopic Findings in Malignant Hypertension , 2006, Journal of clinical hypertension.

[108]  Maciej A Mazurowski,et al.  Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. , 2014, Radiology.

[109]  Scott F. Thompson,et al.  Morphologic blooming in breast MRI as a characterization of margin for discriminating benign from malignant lesions. , 2006, Academic radiology.

[110]  H. Dickhaus,et al.  Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA , 2010, Methods of Information in Medicine.

[111]  Jamal Tuqan,et al.  A state space approach to the design of globally optimal FIR energy compaction filters , 2000, IEEE Trans. Signal Process..

[112]  Hervé Delingette,et al.  LogDemons Revisited: Consistent Regularisation and Incompressibility Constraint for Soft Tissue Tracking in Medical Images , 2010, MICCAI.

[113]  Dimitri Van De Ville,et al.  Machine Learning with Brain Graphs: Predictive Modeling Approaches for Functional Imaging in Systems Neuroscience , 2013, IEEE Signal Processing Magazine.

[114]  Thomas Hofmann,et al.  Support vector machine learning for interdependent and structured output spaces , 2004, ICML.

[115]  M. Vetterli,et al.  Wavelets, subband coding, and best bases , 1996, Proc. IEEE.

[116]  Jonathan Baxter,et al.  A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling , 1997, Machine Learning.

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

[118]  I. Cree,et al.  Ophthalmic Pathology—An Atlas and Textbook , 1998 .

[119]  Yanchun Zhang,et al.  Exploring Sampling in the Detection of Multicategory EEG Signals , 2015, Comput. Math. Methods Medicine.

[120]  H. Bourlard,et al.  Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.

[121]  Doris Y. Tsao,et al.  Faces and objects in macaque cerebral cortex , 2003, Nature Neuroscience.

[122]  Gemma Piella,et al.  A general framework for multiresolution image fusion: from pixels to regions , 2003, Inf. Fusion.

[123]  Roberto Kawakami Harrop Galvão,et al.  Optimized orthonormal wavelet filters with improved frequency separation , 2012, Digit. Signal Process..

[124]  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.

[125]  Paul S. Bradley,et al.  Refining Initial Points for K-Means Clustering , 1998, ICML.

[126]  M. Giger,et al.  Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. , 2006, Medical physics.

[127]  Adam W. Harley An Interactive Node-Link Visualization of Convolutional Neural Networks , 2015, ISVC.

[128]  N. Logothetis What we can do and what we cannot do with fMRI , 2008, Nature.

[129]  Rui Pan,et al.  Terahertz spectra applications in identification of illicit drugs using support vector machines , 2010 .

[130]  E. Hauth,et al.  Quantitative 2- and 3-dimensional analysis of pharmacokinetic model-derived variables for breast lesions in dynamic, contrast-enhanced MR mammography. , 2008, European journal of radiology.

[131]  W. Drevets Neuroimaging studies of mood disorders , 2000, Biological Psychiatry.

[132]  G. Boynton,et al.  Adaptation: from single cells to BOLD signals , 2006, Trends in Neurosciences.

[133]  S Senn,et al.  Analysis of serial measurements in medical research. , 1990, BMJ.

[134]  Yong He,et al.  GRETNA: a graph theoretical network analysis toolbox for imaging connectomics , 2015, Front. Hum. Neurosci..

[135]  Geoffrey E. Hinton,et al.  Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes , 2007, NIPS.

[136]  Leonidas J. Guibas,et al.  Persistence-based segmentation of deformable shapes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[137]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[138]  Jan Modersitzki,et al.  FLIRT with Rigidity—Image Registration with a Local Non-rigidity Penalty , 2008, International Journal of Computer Vision.

[139]  Xiangyu Yang,et al.  Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review , 2011, Journal of biomedicine & biotechnology.

[140]  George Azzopardi,et al.  Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[141]  Lars J. Grimm,et al.  Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms , 2015, Journal of magnetic resonance imaging : JMRI.

[142]  Iasonas Kokkinos,et al.  Deep Filter Banks for Texture Recognition, Description, and Segmentation , 2015, International Journal of Computer Vision.

[143]  N. Just,et al.  Improving tumour heterogeneity MRI assessment with histograms , 2014, British Journal of Cancer.

[144]  Wei Wei,et al.  Comparing Performance of the CADstream and the DynaCAD Breast MRI CAD Systems , 2013, Journal of Digital Imaging.

[145]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[146]  L M Shishlyannikova Application of correlation analysis in psychology , 2009 .

[147]  Yanchun Zhang,et al.  Application of complex extreme learning machine to multiclass classification problems with high dimensionality: A THz spectra classification problem , 2015, Digit. Signal Process..

[148]  Gerard Mourou,et al.  Generation of ultrahigh peak power pulses by chirped pulse amplification , 1988 .

[149]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[150]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[151]  Roberto Kawakami Harrop Galvão,et al.  On the Space of Orthonormal Wavelets: Additional Constraints to Ensure Two Vanishing Moments , 2009, IEEE Signal Processing Letters.

[152]  M. Yaffe,et al.  American Cancer Society Guidelines for Breast Screening with MRI as an Adjunct to Mammography , 2007 .

[153]  Bunyarit Uyyanonvara,et al.  Blood vessel segmentation methodologies in retinal images - A survey , 2012, Comput. Methods Programs Biomed..

[154]  Stefano Soatto,et al.  Class segmentation and object localization with superpixel neighborhoods , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[155]  Youngjin Yoo,et al.  Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation , 2014, MLMI.

[156]  Danilo P. Mandic,et al.  A novel augmented complex valued kernel LMS , 2012, 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[157]  Nikos K Logothetis,et al.  On the nature of the BOLD fMRI contrast mechanism. , 2004, Magnetic resonance imaging.

[158]  S Wang,et al.  T-ray Imaging and Tomography , 2003, Journal of biological physics.

[159]  Yaochun Shen,et al.  Comparison of Terahertz Pulse Imaging and Near-Infrared Spectroscopy for Rapid, Non-Destructive Analysis of Tablet Coating Thickness and Uniformity , 2007, Journal of Pharmaceutical Innovation.

[160]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

[161]  Klaus D. Tönnies,et al.  Local similarity measures for lesion registration in DCE-MRI of the breast , 2012 .

[162]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[163]  Visvanathan Ramesh,et al.  Discrete texture traces: Topological representation of geometric context , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[164]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[165]  Shankar M. Krishnan,et al.  Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter , 2002, IEEE Transactions on Biomedical Engineering.

[166]  J. W. Bowen,et al.  Interferometric Technique for Measuring Terahertz Antenna Phase Patterns , 2013, IEEE Sensors Journal.

[167]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[168]  I Daubechies,et al.  Independent component analysis for brain fMRI does not select for independence , 2009 .

[169]  Peter Saveliev A graph, non-tree representation of the topology of a gray scale image , 2011, Electronic Imaging.

[170]  Gustavo Camps-Valls,et al.  Spatio-Spectral Remote Sensing Image Classification With Graph Kernels , 2010, IEEE Geoscience and Remote Sensing Letters.

[171]  Nathan Intrator,et al.  How to Make a Low-Dimensional Representation Suitable for Diverse Tasks , 1996 .

[172]  G. Parker,et al.  DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents , 2007, British Journal of Cancer.

[173]  N. Logothetis The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[174]  Sebastian Thrun,et al.  Is Learning The n-th Thing Any Easier Than Learning The First? , 1995, NIPS.

[175]  S. Mallat A wavelet tour of signal processing , 1998 .

[176]  Karsten Mueller,et al.  Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI , 2013, Psychiatry Research: Neuroimaging.

[177]  Kotagiri Ramamohanarao,et al.  An effective retinal blood vessel segmentation method using multi-scale line detection , 2013, Pattern Recognit..

[178]  Derek Abbott,et al.  De-noising techniques for terahertz responses of biological samples , 2001 .

[179]  Y. Meyer,et al.  Wavelets and Filter Banks , 1991 .

[180]  Alauddin Bhuiyan,et al.  Progress on retinal image analysis for age related macular degeneration , 2014, Progress in Retinal and Eye Research.

[181]  Philip S. Yu,et al.  A review of heterogeneous data mining for brain disorder identification , 2015, Brain Informatics.

[182]  Geoffrey E. Hinton,et al.  Modeling image patches with a directed hierarchy of Markov random fields , 2007, NIPS.

[183]  Thomas Gärtner,et al.  On Graph Kernels: Hardness Results and Efficient Alternatives , 2003, COLT.

[184]  S. Levine,et al.  Acute Ischemic Stroke in Hospitalized Medicare Patients: Evaluation and Treatment , 2003, Stroke.

[185]  Sergios Theodoridis,et al.  Adaptive Learning in Complex Reproducing Kernel Hilbert Spaces Employing Wirtinger's Subgradients , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[186]  Min-Ying Su,et al.  Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. , 2009, Academic radiology.

[187]  John David Fleig,et al.  Assessment of Feasibility to Use Computer Aided Texture Analysis Based Tool for Parametric Images of Suspicious Lesions in DCE-MR Mammography , 2013, Comput. Math. Methods Medicine.

[188]  Eduardo Bayro-Corrochano,et al.  Clifford Support Vector Machines for Classification, Regression, and Recurrence , 2010, IEEE Transactions on Neural Networks.

[189]  Zhongde Wang Fast algorithms for the discrete W transform and for the discrete Fourier transform , 1984 .

[190]  Djemel Ziou,et al.  Automated feature weighting and random pixel sampling in k-means clustering for terahertz image segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[191]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[192]  Bernhard Preim,et al.  A visual analytics approach to diagnosis of breast DCE-MRI data , 2010, Comput. Graph..

[193]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[194]  J. Gomori,et al.  Breast fibroadenoma: mapping of pathophysiologic features with three-time-point, contrast-enhanced MR imaging--pilot study. , 1999, Radiology.

[195]  R. Douglas,et al.  Neuronal circuits of the neocortex. , 2004, Annual review of neuroscience.

[196]  C. Piccoli,et al.  Contrast-enhanced breast MRI: factors affecting sensitivity and specificity , 1997, European Radiology.

[197]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[198]  W. O'Dell,et al.  Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images. , 2004, Medical physics.

[199]  William P. Winfree,et al.  Technology and Applications of Terahertz Imaging Non‐Destructive Examination: Inspection of Space Shuttle Sprayed On Foam Insulation , 2005 .

[200]  Gerard Mourou,et al.  Compression of amplified chirped optical pulses , 1985 .

[201]  Emanuele Trucco,et al.  Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition , 2013, Pattern Recognit..

[202]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[203]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[204]  Catie Chang,et al.  Applications of Multivariate Pattern Classification Analyses in Developmental Neuroimaging of Healthy and Clinical Populations , 2009, Front. Hum. Neurosci..

[205]  W. Niessen,et al.  Quantification of Heterogeneity as a Biomarker in Tumor Imaging: A Systematic Review , 2014, PloS one.

[206]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[207]  Fang Liu,et al.  Terahertz Time-Domain Spectroscopy Analysis with Wave Atoms Transform , 2011 .

[208]  S. Zaroubi,et al.  Complex denoising of MR data via wavelet analysis: application for functional MRI. , 2000, Magnetic resonance imaging.

[209]  B. Szabó,et al.  Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: comparison with empiric and quantitative kinetic parameters. , 2004, Academic radiology.

[210]  Sudeep Sarkar,et al.  Comparison of edge detectors: a methodology and initial study , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[211]  José M. F. Moura,et al.  Subpixel registration in renal perfusion MR image sequence , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[212]  Bayro-Corrochano Eduardo,et al.  Medical image segmentation, volume representation and registration using spheres in the geometric algebra framework , 2007 .

[213]  Hao He,et al.  Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia , 2015, NeuroImage.

[214]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[215]  Joachim Jonuscheit,et al.  A Feature Set for Enhanced Automatic Segmentation of Hyperspectral Terahertz Images , 2011, 2011 Irish Machine Vision and Image Processing Conference.

[216]  Johan A. K. Suykens,et al.  A kernel-based framework to tensorial data analysis , 2011, Neural Networks.

[217]  Charles McLachlan,et al.  Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent , 2009, Magnetic resonance in medicine.

[218]  Hiroko Yamashita,et al.  Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study , 2015, PloS one.

[219]  Daoqiang Zhang,et al.  Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification , 2014, Human brain mapping.

[220]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[221]  Marti A. Hearst Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[222]  Angela Caunce,et al.  Tracer kinetic model–driven registration for dynamic contrast‐enhanced MRI time‐series data , 2007, Magnetic resonance in medicine.

[223]  Pierre Moulin Wavelet thresholding techniques for power spectrum estimation , 1994, IEEE Trans. Signal Process..

[224]  Marc'Aurelio Ranzato,et al.  Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.

[225]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[226]  M. Moseley,et al.  Efficient simulation of magnetic resonance imaging with Bloch-Torrey equations using intra-voxel magnetization gradients. , 2006, Journal of magnetic resonance.

[227]  Wei Lin,et al.  Respiratory motion‐compensated radial dynamic contrast‐enhanced (DCE)‐MRI of chest and abdominal lesions , 2008, Magnetic resonance in medicine.

[228]  Yi Lin Multicategory Support Vector Machines, Theory, and Application to the Classification of . . . , 2003 .

[229]  C. Doran,et al.  Geometric Algebra for Physicists , 2003 .

[230]  P. V. D. Hof,et al.  System identification with generalized orthonormal basis functions , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[231]  Tamir Hazan,et al.  Sparse image coding using a 3D non-negative tensor factorization , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[232]  R. Freeman,et al.  Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity , 2007, Nature Neuroscience.

[233]  Joe Y. Chang,et al.  Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy , 2005, Physics in medicine and biology.

[234]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[235]  Ephraim Feig,et al.  Fast algorithms for the discrete cosine transform , 1992, IEEE Trans. Signal Process..

[236]  Ian Davidson,et al.  Network discovery via constrained tensor analysis of fMRI data , 2013, KDD.

[237]  Philip S. Yu,et al.  DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages , 2014, SDM.

[238]  David E. Breen,et al.  Level Set Modeling and Segmentation of DT-MRI Brain Data , 2001 .

[239]  H. Wu,et al.  Process analytical technology (PAT): quantification approaches in terahertz spectroscopy for pharmaceutical application. , 2008, Journal of pharmaceutical sciences.

[240]  Jürgen Schürmann,et al.  Pattern classification , 2008 .

[241]  Joseph O. Deasy,et al.  Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay , 2015, Journal of magnetic resonance imaging : JMRI.

[242]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[243]  J. N. Kapur,et al.  Entropy optimization principles with applications , 1992 .

[244]  Leo Dorst The Representation of Rigid Body Motions in the Conformal Model of Geometric Algebra , 2006, Human Motion.

[245]  D. McCormick,et al.  Neocortical Network Activity In Vivo Is Generated through a Dynamic Balance of Excitation and Inhibition , 2006, The Journal of Neuroscience.

[246]  Sillas Hadjiloucas,et al.  Optimization of apodization functions in terahertz transient spectrometry. , 2007, Optics letters.

[247]  Eckhard Hitzer,et al.  Angles Between Subspaces Computed in Clifford Algebra , 2010, 1306.1825.

[248]  Sen,et al.  Debye-Porod law of diffraction for diffusion in porous media. , 1995, Physical review. B, Condensed matter.

[249]  T. Carlson,et al.  Patterns of Activity in the Categorical Representations of Objects , 2003 .

[250]  Michael Unser,et al.  On the optimality of ideal filters for pyramid and wavelet signal approximation , 1993, IEEE Trans. Signal Process..

[251]  Helen Hong,et al.  Deformable lung registration between exhale and inhale CT scans using active cells in a combined gradient force approach. , 2010, Medical physics.

[252]  Jan Stolarek Improving energy compaction of a wavelet transform using genetic algorithm and fast neural network , 2010 .