메뉴 건너뛰기




Volumn 83, Issue , 2013, Pages 148-157

Locally linear embedding (LLE) for MRI based Alzheimer's disease classification

Author keywords

Alzheimer's disease; Classification of AD; Locally linear embedding; MRI; Statistical learning

Indexed keywords

AGED; ALZHEIMER DISEASE; ARTICLE; BRAIN SIZE; CLASSIFIER; CONTROLLED STUDY; CORTICAL THICKNESS (BRAIN); DISEASE CLASSIFICATION; FEMALE; HUMAN; LEARNING ALGORITHM; LOCAL LINEAR EMBEDDING; MAJOR CLINICAL STUDY; MALE; MILD COGNITIVE IMPAIRMENT; NUCLEAR MAGNETIC RESONANCE IMAGING; PARIETAL LOBE; PRIORITY JOURNAL; TEMPORAL LOBE; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; MIDDLE AGED; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

EID: 84881226895     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2013.06.033     Document Type: Article
Times cited : (145)

References (65)
  • 1
    • 84861601363 scopus 로고    scopus 로고
    • Comparative analysis of nonlinear dimensionality reduction techniques for breast mri segmentation
    • Akhbardeh A., Jacobs M. Comparative analysis of nonlinear dimensionality reduction techniques for breast mri segmentation. Med. Phys. 2012, 39:2275-2289.
    • (2012) Med. Phys. , vol.39 , pp. 2275-2289
    • Akhbardeh, A.1    Jacobs, M.2
  • 2
    • 0043278893 scopus 로고    scopus 로고
    • Laplacian eigenmaps and spectral techniques for embedding and clustering
    • MIT Press
    • Belkin M., Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems 2001, 14:585-591. MIT Press.
    • (2001) Advances in Neural Information Processing Systems , vol.14 , pp. 585-591
    • Belkin, M.1    Niyogi, P.2
  • 5
    • 44949127084 scopus 로고    scopus 로고
    • New methods for fMRI data processing based on locally linear embeddings
    • (681412-681412-9)
    • Chaillou L., Yetik I.S., Wernick M.N. New methods for fMRI data processing based on locally linear embeddings. Proceedings of the SPIE 2008, 6814. (681412-681412-9).
    • (2008) Proceedings of the SPIE , vol.6814
    • Chaillou, L.1    Yetik, I.S.2    Wernick, M.N.3
  • 6
    • 33644973537 scopus 로고    scopus 로고
    • Robust locally linear embedding
    • Chang H., Yeung D.Y. Robust locally linear embedding. Pattern Recognit. 2006, 39:1053-1065.
    • (2006) Pattern Recognit. , vol.39 , pp. 1053-1065
    • Chang, H.1    Yeung, D.Y.2
  • 7
    • 36248932499 scopus 로고    scopus 로고
    • Enhancing human face detection by resampling examples through manifolds
    • Chen J., Wang R., Yan S., Shan S., Chen X., Gao W. Enhancing human face detection by resampling examples through manifolds. Trans. Syst. Man Cybern. A 2007, 37:1017-1028.
    • (2007) Trans. Syst. Man Cybern. A , vol.37 , pp. 1017-1028
    • Chen, J.1    Wang, R.2    Yan, S.3    Shan, S.4    Chen, X.5    Gao, W.6
  • 8
    • 84855418467 scopus 로고    scopus 로고
    • Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data
    • Cho Y., Seong J.K., Jeong Y., Shin S.Y. Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 2012, 59:2217-2230.
    • (2012) Neuroimage , vol.59 , pp. 2217-2230
    • Cho, Y.1    Seong, J.K.2    Jeong, Y.3    Shin, S.Y.4
  • 9
    • 84862776712 scopus 로고    scopus 로고
    • Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images
    • Chu C., Hsu A.L., Chou K.H., Bandettini P., Lin C. Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 2012, 60:59-70.
    • (2012) Neuroimage , vol.60 , pp. 59-70
    • Chu, C.1    Hsu, A.L.2    Chou, K.H.3    Bandettini, P.4    Lin, C.5
  • 10
    • 66249143023 scopus 로고    scopus 로고
    • Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI
    • Chupin M., Gérardin E., Cuingnet R., Boutet C., Lemieux L., Lehéricy S., Benali H., Garnero L., Colliot O. Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI. Hippocampus 2009, 19:579-587.
    • (2009) Hippocampus , vol.19 , pp. 579-587
    • Chupin, M.1    Gérardin, E.2    Cuingnet, R.3    Boutet, C.4    Lemieux, L.5    Lehéricy, S.6    Benali, H.7    Garnero, L.8    Colliot, O.9
  • 11
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20:273-297.
    • (1995) Mach. Learn. , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 12
    • 79953032540 scopus 로고    scopus 로고
    • Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment
    • Costafreda S., Dinov I., Tu Z., et al. Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. Neuroimage 2011, 56:212-219.
    • (2011) Neuroimage , vol.56 , pp. 212-219
    • Costafreda, S.1    Dinov, I.2    Tu, Z.3
  • 13
    • 79955059574 scopus 로고    scopus 로고
    • Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database
    • Cuingnet R., Gerardin E., Tessieras J., Auzias G., Lehéricy S., Habert M.O., Chupin M., Benali H., Colliot O. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 2011, 56:766-781.
    • (2011) Neuroimage , vol.56 , pp. 766-781
    • Cuingnet, R.1    Gerardin, E.2    Tessieras, J.3    Auzias, G.4    Lehéricy, S.5    Habert, M.O.6    Chupin, M.7    Benali, H.8    Colliot, O.9
  • 14
    • 4344667151 scopus 로고    scopus 로고
    • Why voxel-based morphometric analysis should be used with great caution when characterizing group differences
    • Davatzikos C. Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. Neuroimage 2004, 23:17-20.
    • (2004) Neuroimage , vol.23 , pp. 17-20
    • Davatzikos, C.1
  • 15
  • 16
    • 84868582487 scopus 로고    scopus 로고
    • Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the adni cohort using patterns of cortical thinning
    • Eskildsen S.F., Coup P., Garca-Lorenzo D., Fonov V., Pruessner J.C., Collins D.L. Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the adni cohort using patterns of cortical thinning. Neuroimage 2013, 65:511-521.
    • (2013) Neuroimage , vol.65 , pp. 511-521
    • Eskildsen, S.F.1    Coup, P.2    Garca-Lorenzo, D.3    Fonov, V.4    Pruessner, J.C.5    Collins, D.L.6
  • 17
    • 38749113910 scopus 로고    scopus 로고
    • Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline
    • Fan Y., Batmanghelich N., Clark C.M., Davatzikos C. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 2008, 39:1731-1743.
    • (2008) Neuroimage , vol.39 , pp. 1731-1743
    • Fan, Y.1    Batmanghelich, N.2    Clark, C.M.3    Davatzikos, C.4
  • 18
    • 67849104993 scopus 로고    scopus 로고
    • Morphological hippocampal markers for automated detection of alzheimer's disease and mild cognitive impairment converters in magnetic resonance images
    • Ferrarini L., Frisoni G.B., Pievani M., Reiber J.H., Ganzola R., Milles J. Morphological hippocampal markers for automated detection of alzheimer's disease and mild cognitive impairment converters in magnetic resonance images. J. Alzheimers Dis. 2009, 17:643-659.
    • (2009) J. Alzheimers Dis. , vol.17 , pp. 643-659
    • Ferrarini, L.1    Frisoni, G.B.2    Pievani, M.3    Reiber, J.H.4    Ganzola, R.5    Milles, J.6
  • 19
    • 79952067946 scopus 로고    scopus 로고
    • Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI)
    • Filipovych R., Davatzikos C. Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). Neuroimage 2011, 55:1109-1119.
    • (2011) Neuroimage , vol.55 , pp. 1109-1119
    • Filipovych, R.1    Davatzikos, C.2
  • 20
    • 9144254529 scopus 로고    scopus 로고
    • Automatically parcellating the human cerebral cortex
    • Fischl B. Automatically parcellating the human cerebral cortex. Cereb. Cortex 2004, 14:11-22.
    • (2004) Cereb. Cortex , vol.14 , pp. 11-22
    • Fischl, B.1
  • 23
    • 0016823810 scopus 로고
    • "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician
    • Folstein M.F., Folstein S.E., McHugh P.R. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 1975, 12:189-198.
    • (1975) J. Psychiatr. Res. , vol.12 , pp. 189-198
    • Folstein, M.F.1    Folstein, S.E.2    McHugh, P.R.3
  • 24
    • 77249176542 scopus 로고    scopus 로고
    • Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters
    • Franke K., Ziegler G., Klöppel S., Gaser C. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 2010, 50:883-892.
    • (2010) Neuroimage , vol.50 , pp. 883-892
    • Franke, K.1    Ziegler, G.2    Klöppel, S.3    Gaser, C.4
  • 25
    • 77950537175 scopus 로고    scopus 로고
    • Regularization paths for generalized linear models via coordinate descent
    • Friedman J., Hastie T., Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 2010, 33:1-22.
    • (2010) J. Stat. Softw. , vol.33 , pp. 1-22
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 26
    • 28444473249 scopus 로고    scopus 로고
    • Supervised nonlinear dimensionality reduction for visualization and classification
    • Geng X., Zhan D.C., Zhou Z.H. Supervised nonlinear dimensionality reduction for visualization and classification. Trans. Syst. Man Cybern. B 2005, 35:1098-1107.
    • (2005) Trans. Syst. Man Cybern. B , vol.35 , pp. 1098-1107
    • Geng, X.1    Zhan, D.C.2    Zhou, Z.H.3
  • 28
    • 80052400583 scopus 로고    scopus 로고
    • The elements of statistical learning: data mining, inference, and prediction
    • Springer, (2nd ed. 2009. corr. 3rd printing 5th printing. edition)
    • Hastie T., Tibshirani R., Friedman J. The elements of statistical learning: data mining, inference, and prediction. Springer Series in Statistics 2009, Springer, (2nd ed. 2009. corr. 3rd printing 5th printing. edition). Second edition.
    • (2009) Springer Series in Statistics
    • Hastie, T.1    Tibshirani, R.2    Friedman, J.3
  • 29
    • 79551576499 scopus 로고    scopus 로고
    • Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population
    • Hinrichs C., Singh V., Xu G., Johnson S.C. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 2011, 55:574-589.
    • (2011) Neuroimage , vol.55 , pp. 574-589
    • Hinrichs, C.1    Singh, V.2    Xu, G.3    Johnson, S.C.4
  • 30
    • 84886393706 scopus 로고    scopus 로고
    • Z-SVM: an SVM for improved classification of imbalanced data
    • Springer, Berlin/Heidelberg, A. Sattar, B. Kang (Eds.) AI 2006: Advances in Artificial Intelligence
    • Imam T., Ting K., Kamruzzaman J. Z-SVM: an SVM for improved classification of imbalanced data. Lecture Notes in Computer Science 2006, vol. 4304:264-273. Springer, Berlin/Heidelberg. A. Sattar, B. Kang (Eds.).
    • (2006) Lecture Notes in Computer Science , vol.4304 , pp. 264-273
    • Imam, T.1    Ting, K.2    Kamruzzaman, J.3
  • 34
    • 34548644125 scopus 로고    scopus 로고
    • Dd-hds: a method for visualization and exploration of high-dimensional data
    • Lespinats S., Verleysen M., Giron A., Fertil G. Dd-hds: a method for visualization and exploration of high-dimensional data. Trans. Neural Netw. 2007, 18:1265-1279.
    • (2007) Trans. Neural Netw. , vol.18 , pp. 1265-1279
    • Lespinats, S.1    Verleysen, M.2    Giron, A.3    Fertil, G.4
  • 35
    • 78049264379 scopus 로고    scopus 로고
    • Local manifold learning-based k - nearest-neighbor for hyperspectral image classification
    • Ma L., Member S., Crawford M.M., Tian J. Local manifold learning-based k - nearest-neighbor for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2010, 48:4099-4109.
    • (2010) IEEE Trans. Geosci. Remote Sens. , vol.48 , pp. 4099-4109
    • Ma, L.1    Member, S.2    Crawford, M.M.3    Tian, J.4
  • 36
    • 78651265609 scopus 로고    scopus 로고
    • Dimensionality reduction of FMRI time series data using locally linear embedding
    • Mannfolk P., Wirestam R., Nilsson M., Sthlberg F., Olsrud J. Dimensionality reduction of FMRI time series data using locally linear embedding. MAGMA 2010, 23:327-338. 10.1007/s10334-010-0204-0.
    • (2010) MAGMA , vol.23 , pp. 327-338
    • Mannfolk, P.1    Wirestam, R.2    Nilsson, M.3    Sthlberg, F.4    Olsrud, J.5
  • 37
    • 58149386194 scopus 로고    scopus 로고
    • Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI
    • Misra C., Fan Y., Davatzikos C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 2009, 44:1415-1422.
    • (2009) Neuroimage , vol.44 , pp. 1415-1422
    • Misra, C.1    Fan, Y.2    Davatzikos, C.3
  • 39
    • 0027425211 scopus 로고
    • The clinical dementia rating (cdr): Current version and scoring rules
    • Morris J.C. The clinical dementia rating (cdr): Current version and scoring rules. Neurology 1993, 43:2412-2414.
    • (1993) Neurology , vol.43 , pp. 2412-2414
    • Morris, J.C.1
  • 40
    • 0018306059 scopus 로고
    • A threshold selection method from gray-level histograms
    • Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1979, 9:62-66.
    • (1979) IEEE Trans. Syst. Man Cybern. , vol.9 , pp. 62-66
    • Otsu, N.1
  • 41
    • 78650220560 scopus 로고    scopus 로고
    • Discrimination of ad and normal subjects from MRI: anatomical versus statistical regions
    • Pelaez-Coca M., Bossa M., Olmos S. Discrimination of ad and normal subjects from MRI: anatomical versus statistical regions. Neurosci. Lett. 2011, 487:113-117.
    • (2011) Neurosci. Lett. , vol.487 , pp. 113-117
    • Pelaez-Coca, M.1    Bossa, M.2    Olmos, S.3
  • 42
    • 70349962974 scopus 로고    scopus 로고
    • Development of a new tool to correlate stroke outcome with infarct topography: a proof-of-concept study
    • Phan T.G., Chen J., Donnan G., Srikanth V., Wood A., Reutens D.C. Development of a new tool to correlate stroke outcome with infarct topography: a proof-of-concept study. Neuroimage 2010, 49:127-133.
    • (2010) Neuroimage , vol.49 , pp. 127-133
    • Phan, T.G.1    Chen, J.2    Donnan, G.3    Srikanth, V.4    Wood, A.5    Reutens, D.C.6
  • 45
    • 84861414424 scopus 로고    scopus 로고
    • Within-subject template estimation for unbiased longitudinal image analysis
    • Reuter M., Schmansky N.J., Rosas H.D., Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 2012, 61:1402-1418.
    • (2012) Neuroimage , vol.61 , pp. 1402-1418
    • Reuter, M.1    Schmansky, N.J.2    Rosas, H.D.3    Fischl, B.4
  • 46
    • 19944404611 scopus 로고    scopus 로고
    • Locally linear embedding for classification
    • Delft University of Technology
    • Ridder D.D., Duin R.P.W. Locally linear embedding for classification. Technical Report PH-2002-01 2002, Delft University of Technology.
    • (2002) Technical Report PH-2002-01
    • Ridder, D.D.1    Duin, R.P.W.2
  • 47
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
    • (2000) Science , vol.290 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 49
    • 84875498311 scopus 로고    scopus 로고
    • An MRI-based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment
    • Spulber G., Simmons A., Muehlboeck J., et al. An MRI-based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment. J. Intern. Med. 2013, 273(4):396-409.
    • (2013) J. Intern. Med. , vol.273 , Issue.4 , pp. 396-409
    • Spulber, G.1    Simmons, A.2    Muehlboeck, J.3
  • 51
    • 73949142211 scopus 로고    scopus 로고
    • Feature fusion using locally linear embedding for classification
    • Sun B.Y., Zhang X.M., Li J., Mao X.M. Feature fusion using locally linear embedding for classification. Trans. Neural Netw. 2010, 21:163-168.
    • (2010) Trans. Neural Netw. , vol.21 , pp. 163-168
    • Sun, B.Y.1    Zhang, X.M.2    Li, J.3    Mao, X.M.4
  • 53
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • Tenenbaum J.B., Silva V.d, Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2323.
    • (2000) Science , vol.290 , pp. 2319-2323
    • Tenenbaum, J.B.1    Silva, V.2    Langford, J.C.3
  • 55
    • 33745906989 scopus 로고    scopus 로고
    • Improved locally linear embedding through new distance computing
    • Springer, Berlin Heidelberg, J. Wang, Z. Yi, J. Zurada, B.L. Lu, H. Yin (Eds.) Advances in neural networks - ISNN 2006
    • Wang H., Zheng J., Yao Z., Li L. Improved locally linear embedding through new distance computing. Lecture Notes in Computer Science 2006, vol. 3971:1326-1333. Springer, Berlin Heidelberg. J. Wang, Z. Yi, J. Zurada, B.L. Lu, H. Yin (Eds.).
    • (2006) Lecture Notes in Computer Science , vol.3971 , pp. 1326-1333
    • Wang, H.1    Zheng, J.2    Yao, Z.3    Li, L.4
  • 58
    • 5044226695 scopus 로고    scopus 로고
    • Unsupervised learning of image manifolds by semidefinite programming
    • CVPR, Computer Vision and Pattern Recognition, 2004 II-988-II-995
    • Weinberger K., Saul L. Unsupervised learning of image manifolds by semidefinite programming. Proceedings of the 2004 IEEE Computer Society Conference on 2004, vol. 2:II-988-II-995. CVPR.
    • (2004) Proceedings of the 2004 IEEE Computer Society Conference on , vol.2
    • Weinberger, K.1    Saul, L.2
  • 60
    • 80054077865 scopus 로고    scopus 로고
    • The Alzheimer's Disease Neuroimaging Initiative Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease
    • Wolz R., Julkunen V., Koikkalainen J., Niskanen E., Zhang D.P., Rueckert D., Soininen H., Ltjnen J., the Alzheimer's Disease Neuroimaging Initiative Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease. PLoS One 2011, 6:e25446.
    • (2011) PLoS One , vol.6
    • Wolz, R.1    Julkunen, V.2    Koikkalainen, J.3    Niskanen, E.4    Zhang, D.P.5    Rueckert, D.6    Soininen, H.7    Ltjnen, J.8
  • 61
    • 79959245254 scopus 로고    scopus 로고
    • Independent componenet analysis-based classification of Alzheimer's disease MRI data
    • Yang W., Lui R., Gao J.H., et al. Independent componenet analysis-based classification of Alzheimer's disease MRI data. J. Alzheimers Dis. 2011, 24:775-783.
    • (2011) J. Alzheimers Dis. , vol.24 , pp. 775-783
    • Yang, W.1    Lui, R.2    Gao, J.H.3
  • 62
    • 60349128863 scopus 로고    scopus 로고
    • Growing locally linear embedding for manifold learning
    • Yin J., Hu D., Zhou Z. Growing locally linear embedding for manifold learning. J. Pattern Recognit. Res. 2007, 1-16.
    • (2007) J. Pattern Recognit. Res.
    • Yin, J.1    Hu, D.2    Zhou, Z.3
  • 63
    • 84863351725 scopus 로고    scopus 로고
    • Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers
    • Zhang D., Shen D. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PLoS One 2012, 7:e33182.
    • (2012) PLoS One , vol.7
    • Zhang, D.1    Shen, D.2
  • 64
    • 79952073234 scopus 로고    scopus 로고
    • Multimodal classification of Alzheimer's disease and mild cognitive impairment
    • Zhang D., Wang Y., Zhou L., Yuan H., Shen D. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage 2011, 55:856-867.
    • (2011) Neuroimage , vol.55 , pp. 856-867
    • Zhang, D.1    Wang, Y.2    Zhou, L.3    Yuan, H.4    Shen, D.5
  • 65
    • 33644662618 scopus 로고    scopus 로고
    • Non-negative matrix factorization with log gabor wavelets for image representation and classification
    • Zheng Z., Jie Y. Non-negative matrix factorization with log gabor wavelets for image representation and classification. J. Syst. Eng. Electron. 2005, 16:738-745.
    • (2005) J. Syst. Eng. Electron. , vol.16
    • Zheng, Z.1    Jie, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.