Multivariate image analysis: A review with applications

Ledley, 1964, High-speed automatic analysis of biomedical pictures, Science, 146, 216, 10.1126/science.146.3641.216 Berstein, 1976, Digital image processing of earth observation sensor data, IBM Journal of Research and Development, 20, 40, 10.1147/rd.201.0040 Sklansky, 1973, Tumor detection in radiographs, Computers and Biomedical Research, 6, 299, 10.1016/0010-4809(73)90066-9 Strauss, 1971, A scintiphotographic method for measuring left ventricular ejection fraction in man without cardiac catheterization, American Journal of Cardiology, 28, 985, 10.1016/0002-9149(71)90100-7 Brayer, 1977, Modelling of Earth Resources Satellite Data. Syntactic Pattern Recognition Applications Cohen, 1991, Automatic inspection of textile fabrics using textural models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 803, 10.1109/34.85670 Goldstein, 1989, time inspection of a large set of surface defects in metal parts, SPIE Automatic Inspection and High Speed Vision Architectures, 849, 184, 10.1117/12.942841 Boukouvalas, 1997, Automatic color grading of ceramic tiles using computer vision, IEEE Transactions on Industrial Electronics, 44, 132, 10.1109/41.557508 Sonka, 2007 González, 1992 Jain, 2000, Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 4, 10.1109/34.824819 Tuceryan, 1998, Texture analysis, 207 Pratt, 2001 Serra, 1982 Haralick, 1979, Statistical and structural approaches to texture, Proceedings of the IEEE, 67, 780, 10.1109/PROC.1979.11328 Porter, 1997, Robust rotation-invariant texture classification: wavelet, gabor filter and GMRF based schemes, IEE Proceedings on Vision Image Signal Processing, 144, 180, 10.1049/ip-vis:19971182 Tsatsanis, 1992, Object and texture classification using higher order statistics, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 733, 10.1109/34.142910 Unser, 1995, Texture classification and segmentation using wavelet frames, IEEE Transactions on Image Processing, 4, 1549, 10.1109/83.469936 B. Vidakovic. Statistical Modeling by Wavelets. Ed. John Wiley & Sons, Inc. New York, 1999. F. López, Real-Time Surface Grading of Ceramic Tiles, PhD Thesis. Technical University of Valencia, Valencia, Spain. 2005. Albregsten, 1995, Statistical texture measures computed from gray level cooccurrence matrices Albregsten, 1995, Statistical texture measures computed from gray level run-length matrices G. Winkler. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. 2nd Edition Ed. Springer, 2003. Sarkar, 1997, A new approach for subset 2-D AR model identification for describing textures, IEEE Transactions on Image Processing, 6, 407, 10.1109/83.557348 Keller, 1989, Texture description and segmentation trough fractal geometry, Computer Vision Graphics Image Processing, 45, 150, 10.1016/0734-189X(89)90130-8 Pentland, 1984, Fractal-based description of natural scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 661, 10.1109/TPAMI.1984.4767591 Rosenfeld, 1970, Texture synthesis, 309 Alsberg, 1997, An introduction to wavelets transforms for chemometricians, Chemometrics and Intelligent Laboratory Systems, 37, 215, 10.1016/S0169-7439(97)00029-4 Walczak, 2000 Song, 1995, Texture crack detection, Machine Vision and Applications, 8, 63, 10.1007/BF01213639 Boukouvalas, 1998, Automatic system for surface inspection and sorting of tiles, Journal of Materials Processing Technology, 82, 179, 10.1016/S0924-0136(98)00044-2 Fioravanti, 1995, Spectral and rank order approaches to texture analysis, European Transactions on Telecomunications, 6, 287, 10.1002/ett.4460060309 G. Van de Wouwer. Wavelets for Multiscale Texture Analysis, PhD Thesis, Department of Physics, University of Antwerp, Belgium., 1998. Materka, 1998, Texture analysis methods — a review Simoncelli, 1992, Shiftable multiscale transforms, IEEE Transactions on Information Theory, 38, 587, 10.1109/18.119725 Freeman, 1991, The design and use of steerable filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 891, 10.1109/34.93808 Tzagkarakis, 2006, Rotation-invariant texture retrieval with Gaussianized steerable pyramids, IEEE Transactions on Image Processing, 15, 2702, 10.1109/TIP.2006.877356 Bovik, 1990, Multichannel texture analysis using localized spatial filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 55, 10.1109/34.41384 Palm, 2004, Color texture classification by integrative co-occurrence matrices, Pattern Recognition, 37, 965, 10.1016/j.patcog.2003.09.010 Jiménez, 2005, Integration of spatial and spectral information, IEEE Transactions on Geoscience and Remote Sensing, 43, 844 Bhuiyan, 2007, Blood vessel segmentation from color retinal images using unsupervised texture classification, Proceedings of the IEEE International Conference on Image Processing, 5, 521 Mäenpää, 2003, Optimising color and texture features for real-time visual inspection, Pattern Analysis and Applications, 6, 169, 10.1007/s10044-002-0179-1 Jackson, 1991 Geladi, 1986, Partial least-squares regression: a tutorial, Analytica Chimica Acta, 185, 1, 10.1016/0003-2670(86)80028-9 Esbensen, 1989, Strategy of multivariate image analysis, Chemometrics and Intelligent Laboratory Systems, 7, 67, 10.1016/0169-7439(89)80112-1 P. Geladi, H. Grahn. Multivariate Image Analysis. Ed. John Wiley & Sons Ltd. Chichester, England., 1996. Geladi, 1989, Can image analysis provide information useful in chemistry?, Journal of Chemometrics, 3, 419, 10.1002/cem.1180030209 Bharati, 2000, Texture analysis of images using Principal Component Analysis, 27 Prats-Montalbán, 2007, Integration of color and textural information in multivariate image analysis: defect detection and classification issues, Journal of Chemometrics, 21, 10, 10.1002/cem.1026 Bruwer, 2008, Dinamic contrast-enhanced MRI diagnosis in oncology via principal component analysis, Journal of Chemometrics, 22, 708, 10.1002/cem.1143 Wold, 1984, PLS regression, 6, 581 Lied, 2001, Principles of MIR, multivariate image regression. I: regression typology and representative application studies, Chemometrics and Intelligent Laboratory Systems, 58, 213, 10.1016/S0169-7439(01)00160-5 Lied, 2000, Multivariate image regression (MIR): implementation of image PLSR-first forays, Journal of Chemometrics, 14, 585, 10.1002/1099-128X(200009/12)14:5/6<585::AID-CEM627>3.0.CO;2-Q Sjöström, 1986, PLS discriminant plots Liu, 2006, Estimation and monitoring of product aesthetics: application to manufacturing of “engineered stone” countertops, Machine Vision and Applications, 16, 374, 10.1007/s00138-005-0009-8 Liu, 2005, Flotation froth monitoring using multiresolutional multivariate image analysis, Minerals Engineering, 18, 65, 10.1016/j.mineng.2004.05.010 Yu, 2003, Multivariate image analysis and regression for prediction of coating and distribution in the production of snack foods, Chemometrics and Intelligent Laboratory Systems, 72, 57 Yu, 2003, Monitoring turbulent nonpremixed flames in an industrial boiler using Multivariate Image Analysis (MIA), 597 Huang, 2000, Applications of Angle Measure Technique (AMT) in image analysis: part I. A new methodology for in situ powder characterization, Chemometrics and Intelligent Laboratory Systems, 54, 1, 10.1016/S0169-7439(00)00100-3 Dahl, 2007, Image analytical determination of particle size distribution characteristics of natural and industrial bulk aggregates, Chemometrics and Intelligent Laboratory Systems, 89, 9, 10.1016/j.chemolab.2007.05.005 Indahl, 1998, Evaluation of alternative spectral feature extraction methods of textural images for multivariate modelling, Journal of Chemometrics, 12, 261, 10.1002/(SICI)1099-128X(199807/08)12:4<261::AID-CEM513>3.0.CO;2-Z Liu, 2007, On the extraction of spectral and spatial information from images, Chemometrics and Intelligent Laboratory Systems, 85, 119, 10.1016/j.chemolab.2006.05.011 Prats-Montalbán, 2008, Multivariate statistical projection methods to perform robust feature extraction and classification in surface grading, Journal of Electronic Imaging, 17, 031106, 10.1117/1.2957886 López, 2008, Feature selection using design of experiments and logistic regression: application to surface grading, Pattern Recognition, 41, 1161, 10.1016/j.patcog.2007.09.011 MIA Code, http://mseg.webs.upv.es/Software.html. Bro, 2008, Cross-validation of component models: a critical look at current methods, Analytical and Bioanalytical Chemistry, 390, 1241, 10.1007/s00216-007-1790-1 Bharati, 2003, Softwood lumber grading through on-line multivariate image analysis techniques, Industrial and Engineering Chemistry Research, 42, 5345, 10.1021/ie0210560 Reis, 2009, Wavelet texture analysis of on-line acquired images for paper formation assessment and monitoring, Chemometrics and Intelligent Laboratory Systems, 95, 129, 10.1016/j.chemolab.2008.09.007 García-Muñoz, 2010, Coating assessment for colored immediate release tablets using multivariate image analysis, International Journal of Pharmaceutics, 395, 104, 10.1016/j.ijpharm.2010.05.026 Ferrer, 2007, Multivariate statistical process control based on principal component analysis (MSPC-PCA): some reflections and a case study in an autobody assembly process, Quality Engineering, 19, 311, 10.1080/08982110701621304 Kourti, 1995, Process analysis, monitoring and diagnosis, using multivariate projection methods, Chemometrics and Intelligent Laboratory Systems, 28, 3, 10.1016/0169-7439(95)80036-9 Nomikos, 1995, Multivariate SPC charts for monitoring batch processes, Technometrics, 37, 41, 10.2307/1269152 Tracy, 1992, Multivariate control charts for individual observations, Journal of Quality Technology, 24, 88, 10.1080/00224065.1992.12015232 Jackson, 1979, Control procedures for residuals associated with principal component analysis, Technometrics, 21, 341, 10.2307/1267757 Daubechies, 1988, Orthonormal bases of compactly supported wavelets, Communications on Pure and Applied Mathematics, XLI, 909, 10.1002/cpa.3160410705 Bharati, 1998, Multivariate image analysis for process monitoring and control, Industrial and Engineering Chemistry Research, 37, 4715, 10.1021/ie980334l Kourti, 1996, Multivariate SPC methods for process and product monitoring, Journal of Quality Technology, 28, 409, 10.1080/00224065.1996.11979699 J.M. Prats-Montalbán, A. Ferrer. Statistical process control based on multivariate image analysis, Journal of Quality Technology (submitted for publication). Wold, 1984, Multivariate data analysis in chemistry J.M. Prats-Montalbán, A. Ferrer, (2009) Applications and benefits of PLS-DA based MIA for isolation and quantification of different kind of features, Journal of Chemometrics (submitted for publication). M.H. Bharati, (2002) Multivariate Image Analysis and Regression for Industrial Process Monitoring and Product Quality Control, Ph.D. Thesis, McMaster University, Hamilton (Ontario), Canada. Chevallier, 2006, Application of PLS-DA in multivariate image analysis, Journal of Chemometrics, 20, 221, 10.1002/cem.994 Pietikäinen, 2000, Rotation-invariant texture classification using feature distributions, Pattern Recognition, 33, 43, 10.1016/S0031-3203(99)00032-1 Yu, 2001, A direct LDA algorithm for high-dimensional data — with application to face recognition, Pattern Recognition, 34, 2067, 10.1016/S0031-3203(00)00162-X Munder, 2006, An experimental study on pedestrian classification, IEEE on Pattern Analysis and Machine Intelligence, 28, 1863, 10.1109/TPAMI.2006.217 Antonelli, 2004, Automated evaluation of food color by means of multivariate image analysis coupled to a wavelet-based classification algorithm, Analytica Chimica Acta, 515, 3, 10.1016/j.aca.2004.01.005 Eriksson, 2005, Multivariate analysis of congruent images (MACI), Journal of Chemometrics, 19, 393, 10.1002/cem.944 Kucheryavski, 2007, Using hard and soft models for classification of medical images, Chemometrics and Intelligent Laboratory Systems, 88, 100, 10.1016/j.chemolab.2006.08.012 Liu, 2005, Modeling and optimization of product appearance: application to injection-molded plastic models, Industrial Chemical Engineering Research, 44, 4687, 10.1021/ie0492101 Tessier, 2008, Estimation of alumina content of anode cover materials using multivariate image analysis, Chemical Engineering Science, 63, 1370, 10.1016/j.ces.2007.07.028 Prats-Montalbán, 2009, Prediction of skin quality properties by different multivariate image analysis methodologies, Chemometrics and Intelligent Laboratory Systems, 96, 6, 10.1016/j.chemolab.2008.10.012 Facco, 2010, Automatic characterization of nanofiber assemblies by image texture analysis, Chemometrics and Intelligent Laboratory Systems, 103, 66, 10.1016/j.chemolab.2010.05.018 Grahn, 2007 2009 de Juan, 2009, Chemometric tools for image analysis, 65 Burger, 2006, Hyperspectral NIR image regression. Part II: dataset preprocessing diagnostics, Journal of Chemometrics, 20, 106, 10.1002/cem.986 Geladi, 1986, Image analysis and chemical information in images, Analytica Chimica Acta, 191, 473, 10.1016/S0003-2670(00)86335-7 Kohler, 2007, MIA of a set of FTIR microspectroscopy images of aged bovine muscle tissue combining image and design information, Analytical and Bioanalytical Chemistry, 389, 1143, 10.1007/s00216-007-1414-9 Geladi, 1991, Regression on multivariate images — principal component regression for modeling, prediction and visual diagnostic-tools, Journal of Chemometrics, 5, 97, 10.1002/cem.1180050206 Esbensen, 1992, Strategies for multivariate image regression, Chemometrics and Intelligent Laboratory Systems, 14, 357, 10.1016/0169-7439(92)80118-N Geladi, 2004, Hyperspectral imaging: calibration problems and solutions, Chemometrics and Intelligent Laboratory Systems, 72, 209, 10.1016/j.chemolab.2004.01.023 Burger, 2006, Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples, The Analyst, 131, 1152, 10.1039/b605386f Chong, 2005, Performance of some variable selection methods when multicollinearity is present, Chemometrics and Intelligent Laboratory Systems, 78, 103, 10.1016/j.chemolab.2004.12.011 Trahn, 2005, Clustering multispectral images: a tutorial, Chemometrics and Intelligent Laboratory Systems, 77, 3, 10.1016/j.chemolab.2004.07.011 Wehrens, 2002, Mixture modelling of medical magnetic resonance data, Journal of Chemometrics, 16, 274, 10.1002/cem.721 Lin, 2006, Characterization of chloramphenicol palmitate drug polymorphs by Raman mapping with multivariate image segmentation using a spatial directed agglomeration clustering method, Analytical Chemistry, 78, 6003, 10.1021/ac0520902 Massart, 1983 Krooshof, 2006, Effects of including spatial information in clustering multivariate image data, TrAC Trends in Analytical Chemistry, 25, 1067, 10.1016/j.trac.2006.09.002 Bharati, 2004, Image texture analysis: methods and comparisons, Chemometrics and Intelligent Laboratory Systems, 72, 57, 10.1016/j.chemolab.2004.02.005 de Juan, 2004, Spectroscopic imaging and chemometrics: a powerful combination for global and local sample analysis, TrAC Trends in Analytical Chemistry, 23, 70, 10.1016/S0165-9936(04)00101-3 Wang, 2003, Application of modified alternating least squares regression to spectroscopic image analysis, Analytica Chimica Acta, 476, 93, 10.1016/S0003-2670(02)01369-7 Batonneau, 2003, Polarization effects of confocal Raman microspectrometry of crystal powders using interactive self-modeling analysis, The Journal of Physical Chemistry, 107, 1502, 10.1021/jp0217536 Windig, 1991, Interactive self modeling mixture analysis, Analytical Chemistry, 63, 1425, 10.1021/ac00014a016 Keller, 1991, Analytica Chimica Acta, 246, 379, 10.1016/S0003-2670(00)80976-9 de Juan, 2005, Local rank analysis for exploratory spectroscopic image analysis. Fixed size image window-evolving factor analysis, Chemometrics and Intelligent Laboratory Systems, 77, 64, 10.1016/j.chemolab.2004.11.006 de Juan, 2003, Chemometrics applied to unravel multicomponent processes and mixtures. Revisiting latest trends in multivariate resolution, Analytica Chimica Acta, 500, 195, 10.1016/S0003-2670(03)00724-4 Tauler, 1995, Multivariate curve resolution applied to second order data, Chemometrics and Intelligent Laboratory Systems, 30, 133, 10.1016/0169-7439(95)00047-X Jaumot, 2005, A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB, Chemometrics and Intelligent Laboratory Systems, 76, 101, 10.1016/j.chemolab.2004.12.007 Hancewicz, 2005, Discriminant image resolution: a novel multivariate image analysis method utilizing a spatial classification constraint in addition to bilinear nonnegativity, Chemometrics and Intelligent Laboratory Systems, 77, 18, 10.1016/j.chemolab.2004.07.013 Hancewicz, 2004, Multivariate image resolution for spectroscopic image analysis de Juan, 2008, Use of local rank-based spatial information for resolution of spectroscopic images, Journal of Chemometrics, 22, 291, 10.1002/cem.1099