Biomedical signal analysis: Contemporary methods and applications

Biomedical Signal Analysis: Contemporary Methods and Applications

Volumn , Issue , 2010, Pages 1-415

  Theis, Fabian J ^a   Meyer Bäse, Anke ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

BIOMEDICAL SIGNAL ANALYSIS; INNOVATIVE METHOD; THEORY AND PRACTICE;

SIGNAL ANALYSIS;

EID: 84895851193     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (301)

1. P. Abdolmaleki, L. Buadu, and H. Naderimansh. Feature extraction and classification of breast cancer on dynamic magnetic resonance imaging using artificial neural network. Cancer Letters, 171(8):183-191, 2001.
2. K. Abed-Meraim and A. Belouchrani. Algorithms for joint block diagonalization. In Proc. EUSIPCO 2004, pages 209-212, 2004.
3. S. Akaho, Y. Kiuchi, and S. Umeyama. MICA: Multimodal independent component analysis. In Proc. IJCNN 1999, pages 927-932, 1999.
4. A. N. Akansu and R. A. Haddad. Multiresolution Signal Decomposition. Academic Press, 1992.
5. M. Akay. Time-Frequency and Wavelets in Biomedical Signal Processing. IEEE Press, 1997.
6. E. Alpaydin. Introduction to Machine Learning. MIT Press, 2004.
7. J. Altman and G. Das. Autoradiographic and histological evidence of postnatal hippocampal neurogenesis in rats. J. Comp. Neurol., 124(3):319-335, 1965.
8. S. Amari. Natural gradient works efficiently in learning. Neural Computation, 10:251-276, 1998.
9. M. Anthony and P. Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge University Press, 1999.
10. K. Arfanakis, D. Cordes, V. Haughton, M. Moritz, M. Quigley, and M. Meyerand. Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets. Magnetic Resonance Imaging, 18(8):921-930, 2000.
11. L. Axel. Cerebral blood flow determination by rapid-sequence computed tomography. Radiology, 137(10):679-686, 1980.
12. F. Bach and M. Jordan. Beyond independent components: trees and clusters. Journal of Machine Learning Research, 4:1205-1233, 2003.
13. F. Bach and M. Jordan. Finding clusters in independent component analysis. In Proc. ICA 2003, pages 891-896, 2003.
14. W. Backfrieder, R. Baumgartner, M. Samal, E. Moser, and H. Bergmann. Quantification of intensity variations in functional mr images using rotated principal components. Phys. Med. Biol., 41(8):1425-1438, 1996.
15. A. Baraldi and P. Blonda. A survey of fuzzy clustering algorithms for pattern recognition-part ii. IEEE Transactions on Systems, Man and Cybernetics, part B, 29(6):786-801, 1999.
16. A. Barnea and F. Nottebohm. Seasonal recruitment of hippocampal neurons in adult free-ranging black-capped chickadees. Proc. Natl. Acad. Sci. USA, 91(23):11217-11221, 1994.
17. M. Bartlett. Face Image Analysis by Unsupervised Learning and Redundancy Reduction. PhD thesis, University of California at San Diego, 1998.
18. M. Bartlett and T. Sejnowski. Independent components of face images: A representation for face recognition. In Proceedings of the 4th Annual Joint Symposium on Neural Computation, 1997.
19. C. Bauer. Independent Component Analysis of Biomedical Signals. Logos Verlag Berlin, 2001.
20. C. Bauer, C. Puntonet, M. Rodriguez-Alvarez, and E. Lang. Separation of EEG signals with geometric procedures. C. Fyfe, ed., Engineering of Intelligent Systems (Proc. EIS 2000), pages 104-108, 2000.
21. C. Bauer, F. Theis, W. Bumler, and E. Lang. Local features in biomedical image clusters extracted with independent component analysis. In Proc. IJCNN 2003, pages 81-84, 2003.
22. H. Bauer. Mass- und Integrationstheorie. Walter de Gruyter, Berlin and New York, 1990.
23. H. Bauer. Wahrscheinlichkeitstheorie. 4th ed. Walter de Gruyter, Berlin and New York, 1990.
24. R. Baumgartner, L. Ryder, W. Richter, R. Summers, M. Jarmasz, and R. Somorjai. Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering versus principal component analysis. Magnetic Resonance Imaging, 18(8):89-94, 2000.
25. A. Bell and T. Sejnowski. An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159, 1995.
26. A. J. Bell and T. J. Sejnowski. An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159, 1995.
27. A. Belouchrani and M. Amin. Blind source separation based on time-frequency signal representations. IEEE Trans. Signal Processing, 46(11):2888-2897, 1998.
28. A. Belouchrani, K. A. Meraim, J.-F. Cardoso, and E. Moulines. A blind source separation technique based on second order statistics. IEEE Transactions on Signal Processing, 45(2):434-444, 1997.
29. S. Ben-Yacoub. Fast object detection using MLP and FFT. IDIAP-RR 11, IDIAP, 1997.
30. A. Benali, I. Leefken, U. Eysel, and E. Weiler. A computerized image analysis system for quantitative analysis of cells in histological brain sections. Journal of Neuroscience Methods, 125:33-43, 2003.
31. J. Bengzon, Z. Kokaia, E. Elmer, A. Nanobashvili, M. Kokaia, and O. Lindvall. Apoptosis and proliferation of dentate gyrus neurons after single and intermittent limbic seizures. Proc. Natl. Acad. Sci. USA, 94:10432-10437, 1997.
32. S. Beucher and C. Lantuéjoul. Use of watersheds in contour detection. In International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation. IRISA Report, Vol. 132, page 132, Rennes, France, 1979.
33. J. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, 1981.
34. C. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
35. B. Biswal, Z. Yetkin, V. Haughton, and J. Hyde. Functional connectivity in the motor cortex of resting human brain using echoplanar MRI. Magnetic Resonance in Medicine, 34(8):537-541, 1995.
36. B. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Proc. COLT 1992, pages 144-152, 1992.
37. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167, 1998.
38. C. Burrus, R. A. Gopinath, and H. Guo. Introduction to Wavelets and Wavelet Transform. Prentice Hall, 1997.
39. R. B. Calinski and J. Harabasz. A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(9):1-27, 1974.
40. H. Cameron, C. Woolley, B. McEwen, and E. Gould. Differentiation of newly born neurons and glia in the dentate gyrus of the adult rat. Neuroscience, 56(2):337-344, 1993.
41. M. Capek, R. Wegenkittl, and P. Felkel. A fully automatic stitching of 2D medical datasets. In J. Jan, J. Kozumplik, and I. Provaznik, editors, BIOSIGNAL 2002: The 16th international EURASIP Conference, pages 326-328, 2002.
42. J. Cardoso and A. Souloumiac. Jacobi angles for simultaneous diagonalization. SIAM J. Mat. Anal. Appl., 17(1):161-164, 1995.
43. J.-F. Cardoso. Multidimensional independent component analysis. In Proc. of ICASSP '98, 1998.
44. J.-F. Cardoso and B. Laheld. Equivariant adaptive source separation. IEEE Transactions on Signal Processing, 44(12):3017-3030, 1996.
45. J.-F. Cardoso and A. Souloumiac. Localization and identification with the quadricovariance. Traitement du Signal, 7(5):397-406, 1990.
46. J.-F. Cardoso and A. Souloumiac. Blind beamforming for non-Gaussian signals. IEE Proceedings-F, 140(6):362-370, 1993.
47. J.-F. Cardoso and A. Souloumiac. Jacobi angles for simultaneous diagonalization. SIAM Journal on Matrix Analysis and Applications, 17:161-164, 1995.
48. K. Castleman. Digital Image Processing. Prentice Hall, 1996.
49. C. Chang, Z. Ding, S. Yau, and F. Chan. A matrix pencil approach to blind source separation of colored nonstationary signals. IEEE Transactions on Signal Processing, 48:900-907, 2000.
50. S. Chatterjee, M. Laudato, and L. Lynch. Genetic algorithms and their statistical applications: An introduction. Computational Statistics and Data Analysis, 22(11):633-651, 11 1996.
51. Z. Cho, J. Jones, and M. Singh. Foundations of Medical Imaging. J. Wiley Intersciece, 1993.
52. S. Choi and A. Cichocki. Blind separation of nonstationary sources in noisy mixtures. Electronics Letters, 36(848-849), 2000.
53. K. Chuang, M. Chiu, C. Lin, and J. Chen. Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy c-means. IEEE Transactions on Medical Imaging, 18(12):1117-1128, 1999.
54. E. Ciaccio, S. Dunn, and M. Akay. Biosignal pattern recognition and interpretation systems: Part I. IEEE Engineering in Medicine and Biology, 13(9):89-97, 1993.
55. E. Ciaccio, S. Dunn, and M. Akay. Biosignal pattern recognition and interpretation systems: Part III. IEEE Engineering in Medicine and Biology, 14(9):129-135, 1994.
56. E. Ciaccio, S. Dunn, and M. Akay. Biosignal pattern recognition and interpretation systems: Part IV. IEEE Engineering in Medicine and Biology, 14(5):269-283, 1994.
57. A. Cichocki and S. Amari. Adaptive blind signal and image processing. John Wiley, 2002.
58. L. Cohen. Time-Frequency Analysis. Prentice Hall, Englewood Cliffs, NJ, 1995.
59. P. Comon. Independent component analysis-a new concept? Signal Processing, 36:287-314, 1994.
60. P. Cosman, R. Gray, and R. Olshen. Evaluating quality of compressed medical images: SNR subjective rating, and diagnostic accuracy. Proc. IEEE, 82(6):919-932, 1994.
61. G. H. D. Rumelhart and J. McClelland. A general framework for parallel distributed processing. Cambridge Press, 1986.
62. G. Darmois. Analyse générale des liaisons stochastiques. Rev. Inst. Internationale Statist., 21:2-8, 1953.
63. R. Dave. Fuzzy shell clustering and applications to circle detection in digital images. International Journal of General Systems, 16(4):343-355, 1990.
64. R. Dave and K. Bhaswan. Adaptive fuzzy c-shells clustering and detection of ellipses. IEEE Transactions on Neural Networks, 3(5):643-662, 1992.
65. S. Davis, M. Fisher, and S. Warach. Magnetic Resonance Imaging in Stroke. Cambridge University Press, Cambridge, 2003.
66. A. Dhawan, Y. Chitre, C. Kaiser-Bonasso, and M. Moskowitz. Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Transaction on Medical Imaging, 15(3):246-259, 1996.
67. A. Dhawan and E. LeRoyer. Mammographic feature enhancement by computerized image processing. Computer Methods and Programs in Biomedicine, 27(1):23-33, 1988.
68. H. Digabel and C. Lantuéjoul. Iterative algorithms. In Actes du Second Symposium Européen d'Analyse Quantitative des Microstructures en Sciences des Matériaux, Biologie et Médecine, pages 85-99. Riederer Verlag, Stuttgart, 1977.
69. F. Dolbeare. Bromodeoxyuridine: A diagnostic tool in biology and medicine, part I: Historical perspectives, histochemical methods and cell kinetics. Histochem. J., 27(5):339-369, 1995.
70. R. Duda and P. Hart. Pattern Classification and Scene Analysis. Wiley, 1973.
71. D. Dumitrescu, B. Lazzerini, and L. Jain. Fuzzy Sets and Their Application to Clustering and Training. CRC Press, 2000.
72. e. a. E. Gould, P. Tanapat. Proliferation of granule cell precursors in the dentate gyrus of adult monkeys is diminished by stress. Proc. Natl. Acad. Sci. USA, 95(6):3168-3171, 1998.
73. J. Eriksson and V. Koivunen. Identifiability and separability of linear ica models revisited. In Proc. of ICA 2003, pages 23-27, 2003.
74. J. Eriksson and V. Koivunen. Complex random vectors and ICA models: Identifiability, uniqueness, and separability. IEEE Transactions on Information Theory, 52(3):1017-1029, 2006.
75. P. Eriksson, E. Perfilieva, T. Bjork-Eriksson, A. Alborn, C. Nordborg, D. Peterson, and F. Gage. Neurogenesis in the adult human hippocampus. Nat. Med., 4(11):1313-1317, 1998.
76. R. Ernst, G. Bodenhausen, and A. Wokaun. Principles of nuclear magnetic resonance in one and two dimensions. Oxford University Press, 1987.
77. F. Esposito, E. Formisano, E. Seifritz, R. Goebel, R. Morrone, G. Tedeschi, and F. D. Salle. Spatial independent component analysis of functional MRI time-series: to what extent do results depend on the algorithm used? Human Brain Mapping, 16(8):146-157, 2002.
78. J. Fan. Overcomplete Wavelet Representations with Applications in Image Processing. PhD thesis, University of Florida, 1997.
79. N. Ferreira and A. Tomé. Blind source separation of temporally correlated signals. In Proc. RECPAD 02, 2002.
80. C. Févotte and F. Theis. Orthonormal approximate joint block-diagonalization. Technical report, GET/Télécom, Paris, 2007.
81. C. Févotte and F. Theis. Pivot selection strategies in Jacobi joint block-diagonalization. In Proc. ICA 2007, volume 4666 of LNCS, pages 177-184. Springer, London, 2007.
82. U. Fischer, V. Heyden, I. Vosshenrich, I. Vieweg, and E. Grabbe. Signal characteristics of malignant and benign lesions in dynamic 2D-MRI of the breast. RoFo, 158(8):287-292, 1993.
83. C. Fisel, J. Ackerman, R. Bruxton, L. Garrido, J. Belliveau, B. Rson, and T. Brady. MR contrast due to microscopically heterogeneous magnetic susceptibility: Numerical simulations and applications to cerebral physiology. Magn. Reson. Med., (6):336-347, 1991.
84. H. Fisher and J. Hennig. Clustering of functional MR data. Proc. ISMRM 4th Ann. Meeting, 96(8):1179-1183, 1996.
85. H. Fisher and J. Hennig. Neural network-based analysis of MR time series. Magnetic Resonance in Medicine, 41(8):124-131, 1999.
86. N. C. for Health Statistics. National Vital Statistics Reports. vol. 6, 1999.
87. J. Friedman and J. Tukey. A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers, 23(9):881-890, 1975.
88. D. Gabor. Theory of communication. Journal of Applied Physiology of the IEE, 93(10):429-457, 1946.
89. I. Gath and A. Geva. Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(3):773-781, 1989.
90. P. Georgiev, P. Pardalos, F. Theis, A. Cichocki, and H. Bakardjian. Data Mining in Biomedicine, chapter Sparse component analysis: a new tool for data mining. Springer, in print, 2005.
91. P. Georgiev and F. Theis. Blind source separation of linear mixtures with singular matrices. In Proc. ICA 2004, volume 3195 of LNCS, pages 121-128. Springer, 2004.
92. C. Gerard and B. Rollins. Chemokines and disease. Nat. Immunol., 2:108-115, 2001.
93. S. Ghurye and I. Olkin. A characterization of the multivariate normal distribution. Ann. Math. Statist., 33:533-541, 1962.
94. S. Goldman and F. Nottebohm. Neuronal production, migration, and differentiation in a vocal control nucleus of the adult female canary brain. Proc. Natl. Acad. Sci. USA, 80(8):2390-2394, 1983.
95. R. C. Gonzalez and R. Woods. Digital Image Processing. Prentice Hall, 2002.
96. M. Goodrich, J. Mitchell, and M. Orletsky. Approximate geometric pattern matching under rigid motions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4):371-379, 1999.
97. C. Goutte, P. Toft, E. Rostrup, F. Nielsen, and L. Hansen. On clustering fmri series. NeuroImage, 9(3):298-310, 1999.
98. T. Graepel and K. Obermayer. A stochastic self-organizing map for proximity data. Neural Computation, 11(7):139-155, 1999.
99. R. Gray. Vector quantization. IEEE ASSP Magazine, 1(1):4-29, 1984.
100. S. Grossberg. Adaptive pattern classification and universal recording. Biological Cybernetics, 23(7):121-134, 1976.
101. S. Grossberg. Competition, decision and consensus. Journal of Mathematical Analysis and Applications, 66:470-493, 7 1978.
102. P. Gruber, C. Kohler, and F. Theis. A toolbox for model-free analysis of fMRI data. In Proc. ICA 2007, volume 4666 of LNCS, pages 209-217. Springer, London, 2007.
103. M. Gudmundsson, E. El-Kwae, and M. Kabuka. Edge detection in medical iamges uisng a genetic algorithm. IEEE Transactions on Medical Imaging, 17(3):469-474, 1998.
104. H. Gutch and F. Theis. Independent subspace analysis is unique, given irreducibility. In Proc. ICA 2007, volume 4666 of LNCS, pages 49-56. Springer, London, 2007.
105. M. Habl. Nichtlineare Analyseverfahren zur Extraction statistisch unabhängiger Komponenten aus multisensorischen EEG-Datensätzen. Diploma Thesis, Institute of Biophysics, University of Regensburg, Germany, 2000.
106. O. Haraldseth, R. Jones, T. Muller, A. Fahlvik, and A. Oksendal. Comparison of DTPA, BMA and superparamagnetic iron oxide particles as susceptibility contrast agents for perfusion imaging of regional cerebral ischemia in the rat. J. Magn. Reson. Imaging, (8):714-717, 1996.
107. K. Haris, S. N. Efstratiadis, N. Maglaveras, and A. Katsaggelos. Hybrid image segmentation using watershed and fast region merging. IEEE Trans. Img. Proc., 7(12):1684-1699, 1998.
108. D. Hartl, M. Griese, R. Gruber, D. Reinhardt, D. Schendel, and S. Krauss-Etschmann. Expression of chemokine receptors ccr5 and cxcr3 on t cells in bronchoalveolar lavage and peripheral blood in pediatric pulmonary diseases. Immunobiology, 206(1-3):224-225, 2002.
109. E. J. Hartman, J. D. Keeler, and J. M. Kowalski. Layered neural networks with Gaussian hidden units as universal approximations. Neural Computation, 2(2):210-215, 1990.
110. S. Haykin. Neural Networks. Macmillan College Publishing, 1994.
111. S. Haykin. Neural networks. Macmillan College Publishing Company, 1994.
112. J. Hérault and C. Jutten. Space or time adaptive signal processing by neural network models. In J. Denker, editor, Neural Networks for Computing: Proceedings of the AIP Conference, pages 206-211, New York, 1986. American Institute of Physics.
113. J. Hertz, A. Krogh, and R. Palmer. Introduction to the Theory of Neural Computation. Addison-Wesley Publishing Company, Redwood City, 1991.
114. H. Herzog. Basic ideas and principles for quantifying regional blood flow with nuclear medical techniques. Nuklearmedizin, (5):181-185, 1996.
115. S. Heywang, A. Wolf, and E. Pruss. MRI imaging of the breast: Fast imaging sequences with and without gd-DTPA. Radiology, 170(2):95-103, 1989.
116. J. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.
117. J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science, USA, 79(8):2554-2558, 1982.
118. J. Hopfield and D. Tank. Computing with neural circuits: A model. Science, 233(4764):625-633, 1986.
119. K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward networks are universal approximators. Neural Networks, 2:359-366, 1989.
120. A. Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3):626-634, 1999.
121. A. Hyvärinen and P. Hoyer. Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces. Neural Computation, 12(7):1705-1720, 2000.
122. A. Hyvärinen, P. Hoyer, and M. Inki. Topographic independent component analysis. Neural Computation, 13(7):1525-1558, 2001.
123. A. Hyvärinen, J. Karhunen, and E. Oja. Independent Component Analysis. Wiley Interscience, 2001.
124. A. Hyvärinen and E. Oja. A fast fixed-point algorithm for independent component analysis. Neural Computation, 9:1483-1492, 1997.
125. A. Hyvärinen and P. Pajunen. On existence and uniqueness of solutions in nonlinear independent component analysis. In Proceedings of the 1998 IEEE International Joint Conference on Neural Networks (IJCNN '98), vol. 2:1350-1355, 1998.
126. A. Ilin. Independent dynamics subspace analysis. In Proc. ESANN 2006, pages 345-350, 2006.
127. C. Jutten, J. Hérault, P. Comon, and E. Sorouchiary. Blind separation of sources, parts I, II and III. Signal Processing, 24:1-29, 1991.
128. A. Kagan, Y. Linnik, and C. Rao. Characterization Problems in Mathematical Statistics. Wiley, New York, 1973.
129. S. Karako-Eilon, A. Yeredor, and D. Mendlovic. Blind Source Separation Based on the Fractional Fourier Transform. In Proc. ICA 2003, pages 615-620, 2003.
130. N. Karayiannis. A methodology for constructing fuzzy algorithms for learning vector quantization. IEEE Transactions on Neural Networks, 8(3):505-518, 1997.
131. N. Karayiannis and P. Pai. Fuzzy algorithms for learning vector quantization. IEEE Transactions on Neural Networks, 7(5):1196-1211, 1996.
132. J. Karhunen and S. Malaroiu. Local independent component analysis using clustering. In Proc. First Int. Workshop on Independent Component Analysis and Blind Signal Separation(ICA99), pages 43-49, 1999.
133. J. Karvanen and F. Theis. Spatial ICA of fMRI data in time windows. In Proc. MaxEnt 2004, volume 735 of AIP Conference Proceedings, pages 312-319, 2004.
134. I. Keck, F. Theis, P. Gruber, E. Lang, K. Specht, G. Fink, A. Tomé, and C. Puntonet. Automated clustering of ICA results for fMRI data analysis. In Proc. CIMED 2005, pages 211-216, Lisbon, Portugal, 2005.
135. I. Keck, F. Theis, P. Gruber, E. Lang, K. Specht, and C. Puntonet. 3D spatial analysis of fMRI data on a word perception task. In Proc. ICA 2004, volume 3195 of LNCS, pages 977-984. Springer, 2004.
136. G. Kempermann, H. Kuhn, and F. Gage. More hippocampal neurons in adult mice living in an enriched environment. Nature, 386(6624):493-495, 1997.
137. R. Kennan, J. Zhong, and J. Gore. Intravascular susceptibility contrast mechanism in tissues. Magn. Reson. Med., pages 9-21, 6 1994.
138. D. J. Kim, Y. W. Park, and D. J. Park. A novel validity index for determination of the optimal number of clusters. IEICE Transactions on Inf. and Syst., E84-D(2):281-285, 2001.
139. T. Kohonen. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1):59-69, 1982.
140. T. Kohonen. Self-organized formation of topologically correct feature maps. Biol. Cybern., 43:59-69, 1982.
141. T. Kohonen. Self-Organization and Associative Memory. Springer Verlag, 1988.
142. T. Kohonen, J. Hynninen, J. Kangas, and J. Laaksonen. Som pak: The self-organizing map program package. Helsinki University of Technology, Technical Report A31, 1996.
143. B. Kosko. Adaptive bidirectional associative memory. Applied Optics, 26(9):4947-4960, 1987.
144. C. Kotropoulos, X. Magnisalis, I. Pitas, and M. Strintzis. Nonlinear ultrasonic image processing based on signal-adaptive filters and self-organizing neural networks. IEEE Transaction on Image Processing, 3(1):65-77, 1994.
145. C. K. Kuhl, P. Mielcareck, S. Klaschik, C. Leutner, E. Wardelmann, J. Gieseke, and H. Schild. Dynamic breast MR imaging: Are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology, 211(1):101-110, 1999.
146. H. Kuhn, H. Dickinson-Anson, and F. Gage. Neurogenesis in the dentate gyrus of the adult rat: Age-related decrease of neuronal progenitor proliferation. J. Neurosci., 16(6):2027-2033, 1996.
147. H. Kuhn, T. Palmer, and E. Fuchs. Adult neurogenesis: A compensatory mechanism for neuronal damage. Eur. Arch. Psychiatry Clin. Neurosci., 251(4):152-158, 2001.
148. O. Lange, A. Meyer-Baese, M. Hurdal, and S. Foo. A comparison between neural and fuzzy cluster analysis techniques for functional MRI. Biomedical Signal Processing and Control, 1(3):243-252, 2006.
149. N. Lassen and W. Perl. Tracer Kinetic Methods in Medical Physiology. Raven Press, New York, 1979.
150. D. Lee and H. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401:788-791, 1999.
151. S. Lee and R. M. Kil. A Gaussian Potential Function Network with Hierarchically Self-Organizing Learning. Neural Networks, 4(9):207-224, 1991.
152. T. Lee, M. Girolami, and T. Sejnowski. Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources. Neural Computation, 11:417-441, 1999.
153. C. Leondes. Image Processing and Pattern Recognition. Academic Press, 1998.
154. A. Levin, A. Zomet, S. Peleg, and Y. Weiss. Seamless Image Stitching in the Gradient Domain. Technical Report 2003-82, Leibniz Center, Hebrew University, Jerusalem, 2003.
155. J. Lin. Factorizing multivariate function classes. In Advances in Neural Information Processing Systems, volume 10, pages 563-569. MIT Press, 1998.
156. Y. Linde, A. Buzo, and R. Gray. An algorithm for vector quantizer design. IEEE Transactions on Communications, 28(3):84-95, 1980.
157. R. Linsker. An application of the principle of maximum information preservation to linear systems. Advances in Neural Information Processing Systems, 1, MIT Press, 1989.
158. R. Linsker. Local synaptic learning rules suffice to maximize mutual information in a linear network. Neural Computation, 4:691-702, 1992.
159. R. P. Lippman. An introduction to computing with neural networks. IEEE ASSP Magazine, 4(4):4-22, 1987.
160. Lo, Leung, and Litva. Separation of a mixture of chaotic signals. In Proc. Int. Conf. Accustics, Speech and Signal Processing, pages 1798-1801, 1996.
161. E. Lucht, S. Delorme, and G. Brix. Neural network-based segmentation of dynamic (MR) mammography images. Magnetic Resonance Imaging, 20(8):89-94, 2002.
162. E. Lucht, M. Knopp, and G. Brix. Classification of signal-time curves from dynamic (MR) mammography by neural networks. Magnetic Resonance Imaging, 19(8):51-57, 2001.
163. D. MacKay. Information Theory, Inference, and Learning Algorithms. 6th ed. Cambridge University Press, 2003.
164. A. Macovski. Medical Imaging Systems. Prentice Hall, 1983.
165. S. Mallat. A Wavelet Tour of Signal Processing. Academic Press, 1997.
166. T. Martinetz, S. Berkovich, and K. Schulten. Neural gas network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks, 4(4):558-569, 1993.
167. W. McCulloch and W. Pitts. A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115-133, 1943.
168. M. McKeown, T. Jung, S. Makeig, G. Brown, S. Kindermann, T. Lee, A. Bell, and T. Sejnowski. Spatially independent activity patterns in functional magnetic resonance imaging data during the stroop color-naming task. Proc. Natl. Acad. Sci. USA, 95(8):803-810, 1998.
169. M. McKeown, S. Makeig, G. Brown, T. Jung, S. Kindermann, A. Bell, and T. Sejnowski. Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6:160-188, 1998.
170. M. McKeown, S. Makeig, G. Brown, T. Jung, S. Kindermann, A. Bell, and T. Sejnowski. Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6(8):160-188, 1998.
171. L. A. Meinel, A. Stolpen, K. Berbaum, L. Fajardo, and J. Reinhardt. Breast MRI lesion classification: Improved performance of human readers with a backpropagation network computer-aided diagnosis (CAD) system. Journal of Magnetic Resonance Imaging, 25(1):89-95, 2007.
172. C. Metz. ROC methodology in radiologic imaging. Invest. Radiol., 21(6):720-733, 1986.
173. A. Meyer-Bäse. Pattern Recognition for Medical Imaging. Elsevier Science/Academic Press, 2003.
174. A. Meyer-Bäse, F. Theis, O. Lange, and C. Puntonet. Tree-dependent and topographic-independent component analysis for fMRI analysis. In Proc. ICA 2004, volume 3195 of LNCS, pages 782-789. Springer, 2004.
175. A. Meyer-Bäse, F. Theis, O. Lange, and A. Wismüller. Clustering of dependent components: A new paradigm for fMRI signal detection. In Proc. IJCNN 2004, pages 1947-1952, 2004.
176. Z. Michalewicz. Genetic Algorithms. Springer Verlag, 1995.
177. T. Mitchell. Machine Learning. McGraw Hill, 1997.
178. L. Molgedey and H. Schuster. Separation of a mixture of independent signals using time-delayed correlations. Physical Review Letters, 72(23):3634-3637, 1994.
179. J. Moody and C. Darken. Fast learning in networks of locally-tuned processing units. Neural Computation, 1(2):281-295, 1989.
180. E. Moreau. A generalization of joint-diagonalization criteria for source separation. IEEE Transactions on Signal Processing, 49(3):530-541, 2001.
181. M. Moseley, Z. Vexler, and H. Asgari. Comparison of Gd- and Dy-chelates for T2* contrast-enhanced imaging. Magn. Reson. Med., 22(6):259-264, 1991.
182. K.-R. Müller, P. Philips, and A. Ziehe. JADETD: Combining higher-order statistics and temporal information for blind source separation (with noise). In Proc. of ICA 1999, pages 87-92, 1999.
183. T. Nattkemper, H. Ritter, and W. Schubert. A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections. IEEE Trans. ITB, 5:138-149, 2001.
184. T. Nattkemper, T. Twellmann, H. Ritter, and W. Schubert. Human vs. machine: Evaluation of fluorescence micrographs. Computers in Biology and Medicine, 33:31-43, 2003.
185. S. Ngan and X. Hu. Analysis of fMRI imaging data using self-organizing mapping with spatial connectivity. Magn. Reson. Med., 41:939-946, 8 1999.
186. N. Nilsson. Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw-Hill, 1965.
187. S. Ogawa, T. Lee, and B. Barrere. The sensitivity of magnetic resonance image signals of a rat brain to changes in the cerebral venous blood oxygenation activation. Magn. Reson. Med., 29(8):205-210, 1993.
188. S. Ogawa, T. Lee, A. Kay, and D. Tank. Brain magnetic-resonance-imaging with contrast dependent on blood oxygenation. Proc. Nat. Acad. Sci. USA, 87:9868-9872, 1990.
189. S. Ogawa, D. Tank, R. Menon, and et. al. Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences, 89(8):5951-5955, 1992.
190. A. Oppenheim and R. Schafer. Digital Signal Processing. Prentice Hall, 1975.
191. S. Osowski, T. Markiewicz, B. Marianska, and L. Moszczyński. Feature generation for the cell image recognition of myelogenous leukemia. In Proc. EUSICPO 2004, pages 753-756, 2004.
192. L. Østergaard, A. Sorensen, K. Kwong, R. Weisskopf, C. Gyldensted, and B. Rosen. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: Experimental comparison and preliminary results. Magnetic Resonance in Medicine, 36(10):726-736, 1996.
193. N. Pal, J. Bezdek, and E. Tsao. Generalized clsutering networks and Kohonen's self-organizing scheme. IEEE Transactions on Neural Networks, 4(9):549-557, 1993.
194. S. Pal and S. Mitra. Neuro-Fuzzy Pattern Recognition. JWiley, 1999.
195. A. Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill, 1986.
196. J. Parent, T. Yu, R. Leibowitz, D. Geschwind, R. Sloviter, and D. Lowenstein. Dentate granule cell neurogenesis is increased by seizures and contributes to aberrant network reorganization in the adult rat hippocampus. J. Neursci., 17:3727-3738, 1997.
197. J. Park and I. Sandberg. Universal approximation using radial-basis-function networks. Neural Computation, 3(6):247-257, 1991.
198. K. Pearson. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 6th ser., 2:559-572, 1901.
199. S. Peltier, T. Polk, and D. Noll. Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm. Human Brain Mapping, 20(4):220-226, 2003.
200. H. Penzkofer. Entwicklung von Methoden zur magnetresonanztomographischen Bestimmung der myokardialen und zerebralen Perfusion. PhD thesis, LMU Munich, 1998.
201. N. Petrick, H. Chan, B. Sahiner, M. Helvie, M. Goodsitt, and D. Adler. Computer-aided breast mass detection: False positive reducing using breast tissue composition. Excerpta Medica, 1119(6):373-378, 1996.
202. D.-T. Pham. Joint approximate diagonalization of positive definite matrices. SIAM Journal on Matrix Anal. and Appl., 22(4):1136-1152, 2001.
203. D.-T. Pham and J.-F. Cardoso. Blind separation of instantaneous mixtures of nonstationary sources. IEEE Transactions on Signal Processing, 49(9):1837-1848, 2001.
204. C. Piccoli. Contrast-enhanced breast MRI: Factors affecting sensitivity and specificity. European Radiology, 7(2):281-288, 1997.
205. E. Pietka, A. Gertych, and K. Witko. Informatics infrastructure of CAD system. Computerized Medical Imaging and Graphics, 29:157-169, 10 2005.
206. J. Platt. A resource-allocating network for function interpolation. Neural Computation, 3:213-225, 6 1991.
207. B. Poczos and A. Lörincz. Independent subspace analysis using k-nearest neighborhood distances. In Proc. ICANN 2005, volume 3696 of LNCS, pages 163-168. Springer, 2005.
208. T. Poggio and F. Girosi. Extensions of a theory of networks for approximations and learning: Outliers and negative examples. Touretky's Connectionist Summer School, 3(6):750-756, 1990.
209. T. Poggio and F. Girosi. Networks and the best approximation property. Biological Cybernetics, 63(2):169-176, 1990.
210. T. Poggio and F. Girosi. Networks for approximation and learning. Proceedings of the IEEE, 78(9):1481-1497, 1990.
211. W. Pratt. Digital Image Processing. Wiley, 1978.
212. F. P. Preparata and M. I. Shamos. Computational Geometry: An Introduction. Springer, 1988.
213. W. Press, B. Flannery, S. Teukolsky, and W. Vetterling. Numerical Recipes in C. Cambridge University Press, Cambridge, 1992.
214. P. Pudil, J. Novovicova, and J. Kittler. Floating search methods in feature selection. Pattern Recognition Letters, 15(11):1119-1125, 1994.
215. C. Puntonet, M. Alvarez, A. Prieto, and B. Prieto. Separation of speech signals for nonlinear mixtures. vol. 1607 (II) of LNCS, 1607(II):665-673, 1999.
216. C. Puntonet, C. Bauer, E. Lang, M. Alvarez, and B. Prieto. Adaptive-geometric methods: Application to the separation of EEG signals. P.Pajunen and J.Karhunen, eds., Independent Component Analysis and Blind Signal Separation (Proc. ICA'2000), pages 273-278, 2000.
217. C. Puntonet and A. Prieto. An adaptive geometrical procedure for blind separation of sources. Neural Processing Letters, 2:23-27, 1995.
218. C. Puntonet and A. Prieto. Neural net approach for blind separation of sources based on geometric properties. Neurocomputing, 18:141-164, 1998.
219. W. Reith, S. Heiland, G. Erb, T. Brenner, M. Forsting, and K. Sartor. Dynamic contrast-enhanced T2*-weighted MRI in patients with cerebrovascular disease. Neuroradiology, 30(6):250-257, 1997.
220. K. Rempp, G. Brix, F. Wenz, C. Becker, F. Gückel, and W. Lorenz. Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging. Radiology, 193(10):637-641, 1994.
221. G. Ritter and J. Wilson. Handbook of Computer Vision Algorithms in Image Algebra. CRC Press, 1996.
222. J. Roerdink and A. Meijster. The watershed transform: Definitions, algorithms and parallelization strategies. Fundamenta Informaticae, 41(1):187-228, 2001.
223. B. Rosen, J. Belliveau, J. Vevea, and T. Brady. Perfusion imaging with NMR contrast agents. Magnetic Resonance in Medicine, 14(10):249-265, 1990.
224. D. Rossi and A. Zlotnik. The biology of chemokines and their receptors. Annu. Rev. Immunol., 18:217-242, 2000.
225. D. L. Ruderman. The statistics of natural images. Network, 5:517-548, 1994.
226. G. Scarth, M. McIntrye, B. Wowk, and R. Samorjai. Detection of novelty in functional imaging using fuzzy clustering. Proc. SMR 3rd Annu. Meeting, 95:238-242, 8 1995.
227. R. Schalkoff. Pattern Recognition. Wiley, 1992.
228. I. Schießl, H. Schöner, M. Stetter, A. Dima, and K. Obermayer. Regularized second order source separation. In Proc. ICA 2000, volume 2, pages 111-116, 2000.
229. B. Schölkopf and A. Smola. Learning with Kernels. MIT Press, Cambridge, Mass., 2002.
230. H. Schöner, M. Stetter, I. Schießl, J. Mayhew, J. Lund, N. McLoughlin, and K. Obermayer. Application of blind separation of sources to optical recording of brain activity. In Advances in Neural Information Procession Systems, volume 12, pages 949-955. MIT Press, 2000.
231. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Machine Intell., 22(8):888-905, 2000.
232. W. Siedlecki and J. Sklansky. A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters, 10(11):335-347, 1989.
233. V. Skitovitch. On a property of the normal distribution. DAN SSSR, 89:217-219, 1953.
234. V. Skitovitch. Linear forms in independent random variables and the normal distribution law. Izvestiia AN SSSR, ser. matem., 18:185-200, 1954.
235. A. Souloumiac. Blind source detection using second order non-stationarity. In Proc. Int. Conf. Acoustics, Speech and Signal Processing, pages 1912-1916, 1995.
236. K. Specht and J. Reul. Function segregation of the temporal lobes into highly differentiated subsystems for auditory perception: An auditory rapid event-related fMRI-task. NeuroImage, 20:1944-1954, 2003.
237. K. Stadlthanner, A. Tomé, F. Theis, W. Gronwald, H.-R. Kalbitzer, and E. Lang. Blind source separation of water artifacts in NMR spectra using a matrix pencil. In Proc. ICA 2003, pages 167-172, 2003.
238. K. Stadlthanner, A. Tomé, F. Theis, W. Gronwald, H.-R. Kalbitzer, and E. Lang. Removing water artefacts from 2D protein NMR spectra using GEVD with congruent matrix pencils. In Proc. ISSPA 2003, volume 2, pages 85-88, 2003.
239. K. Stadlthanner, A. Tomé, F. Theis, and E. Lang. A generalized eigendecomposition approach using matrix pencils to remove artifacts from 2d NMR spectra. In Proc. IWANN 2003, volume 2687 of LNCS, pages 575-582. Springer, 2003.
240. G. Stewart. Researches on the circulation time in organs and on the influences which affect it. J. Physiol., 6:1-89, 1894.
241. J. Stone, J. Porrill, N. Porter, and I. Wilkinson. Spatiotemporal independent component analysis of event-related fMRI data using skewed probability density functions. NeuroImage, 15(2):407-421, 2002.
242. J. Sychra, P. Bandettini, N. Bhattacharya, and Q. Lin. Synthetic images by subspace transforms I. Principal components images and related filters. Med. Phys., 21(8):193-201, 1994.
243. M. Tervaniemi and T. van Zuijen. Methodologies of brain research in cognitive musicology. Journal of New Music Research, 28(3):200-208, 1999.
244. F. Theis. Nichtlineare ICA mit Musterabstossung. Master's thesis, Institute of Biophysics, University of Regensburg, Germany, 2000.
245. F. Theis. Mathematics in Independent Component Analysis. Logos Verlag, Berlin, 2002.
246. F. Theis. A new concept for separability problems in blind source separation. Neural Computation, 16:1827-1850, 2004.
247. F. Theis. Uniqueness of complex and multidimensional independent component analysis. Signal Processing, 84(5):951-956, 2004.
248. F. Theis. Uniqueness of real and complex linear independent component analysis revisited. In Proc. EUSIPCO 2004, pages 1705-1708, 2004.
249. F. Theis. Blind signal separation into groups of dependent signals using joint block diagonalization. Proc. ISCAS 2005, pages 5878-5881, 2005.
250. F. Theis. Multidimensional independent component analysis using characteristic functions. In Proc. EUSIPCO 2005, 2005.
251. F. Theis. Towards a general independent subspace analysis. Proc. NIPS 2006, 2007.
252. F. Theis, C. Bauer, and E. Lang. Comparison of maximum entropy and minimal mutual information in a nonlinear setting. Signal Processing, 82:971-980, 2002.
253. F. Theis, P. Gruber, I. Keck, and E. Lang. A robust model for spatiotemporal dependencies. Neurocomputing, 71(10-12):2209-2216, 2008.
254. F. Theis, P. Gruber, I. Keck, A. Meyer-Bäse, and E. Lang. Spatiotemporal blind source separation using double-sided approximate joint diagonalization. Proc. EUSIPCO 2005, 2005.
255. F. Theis, P. Gruber, I. Keck, A. Tomé, and E. Lang. A spatiotemporal second-order algorithm for fMRI data analysis. Proc. CIMED 2005, pages 194-201, 2005.
256. F. Theis, D. Hartl, S. Krauss-Etschmann, and E. Lang. Adaptive signal analysis of immunological data. In Proc. Int. Conf. Information. Fusion 2003, pages 1063-1069, 2003.
257. F. Theis, D. Hartl, S. Krauss-Etschmann, and E. Lang. Neural network signal analysis in immunology. In Proc. ISSPA 2003, volume 2, pages 235-238, 2003.
258. F. Theis and Y. Inouye. On the use of joint diagonalization in blind signal processing. In Proc. ISCAS 2006, 2006.
259. F. Theis, A. Jung, C. Puntonet, and E. Lang. Linear geometric ICA: Fundamentals and algorithms. Neural Computation, 15:419-439, 2003.
260. F. Theis, Z. Kohl, H. Kuhn, H. Stockmeier, and E. Lang. Automated counting of labelled cells in rodent brain section images. Proc. BioMED 2004, pages 209-212, 2004.
261. F. Theis and E. Lang. Maximum entropy and minimal mutual information in a nonlinear model. In Proc. ICA 2001, pages 669-674, 2001.
262. F. Theis, A. Meyer-Bäse, and E. Lang. Second-order blind source separation based on multi-dimensional autocovariances. In Proc. ICA 2004, volume 3195 of LNCS, pages 726-733. Springer, 2004.
263. F. Theis and T. Tanaka. A fast and efficient method for compressing fMRI data sets. In Proc. ICANN 2005, part 2, volume 3697 of LNCS, pages 769-777. Springer, 2005.
264. S. Theodoridis and K. Koutroumbas. Pattern Recognition. Academic Press, 1998.
265. H. Thompson, C. Starmer, R. Whalen, and D.McIntosh. Indicator transit time considered as a gamma variate. Circ. Res., 14(6):502-515, 1964.
266. A. Tomé. Blind source separation using a matrix pencil. In Int. Joint Conf. on Neural Networks (IJCNN), Como, Italy, 2000.
267. A. Tomé. An iterative eigendecomposition approach to blind source separation. In Proc. 3rd Int. Conf. on Independent Component Analysis and Signal Separation, pages 424-428, 2001.
268. A. Tomé and N. Ferreira. On-line source separation of temporally correlated signals. In Proc. EUSIPCO' 02, Toulouse, France, 2002.
269. L. Tong, Y. Inouye, V. Soon, and Y.-F. Huang. Indeterminacy and identifiability of blind identification. IEEE Trans. on Circuits and Systems, 38:499-509, 1991.
270. L. Tong, R.-W. Liu, V. Soon, and Y.-F. Huang. Indeterminacy and identifiability of blind identification. IEEE Transactions on Circuits and Systems, 38:499-509, 1991.
271. G. Torheim, F. Godtliebsen, D. Axelson, K. Kvistad, O. Haraldseth, and P. Rinck. Feature extraction and classification of dynamic contrast-enhanced T2-weighted breast image data. IEEE Transactions on Medical Imaging, 20(12):1293-1301, 2001.
272. M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3:71-86, 1991.
273. A. van der Veen and A. Paulraj. An analytical constant modulus algorithm. IEEE Trans. Signal Processing, 44(5):1-19, 1996.
274. H. van Praag, A. Schinder, B. Christie, N. Toni, T. Palmer, and F. Gage. Functional neurogenesis in the adult hippocampus. Nature, 415(6875):1030-1034, 2002.
275. A. Villringer, B. Rosen, J. Belliveau, J. Ackerman, R. Lauffer, R. Buxton, Y.-S. Chao, V. Wedeen, and T. B. TJ. Dynamic imaging of lanthanide chelates in normal brain: Changes in signal intensity due to susceptibility effects. Magn. Reson. Med., 6:164-174, 1988.
276. R. Vollgraf and K. Obermayer. Multi-dimensional ICA to separate correlated sources. In Proc. Advances in Neural INformation Processing Systems2001, pages 993-1000. MIT Press, 2001.
277. C. von der Malsburg. Self-organization of orientation sensitive cells in striata cortex. Kybernetik 14, (7):85-100, 1973.
278. D. Walnut. An Introduction to Wavelet Analysis. Birkhäuser, 2002.
279. P. Wasserman. Advanced Methods in Neural Computing. VanNostrand Reinhold, New York, 1993.
280. S. Webb. The Physics of Medical Imaging. Adam Hilger, 1990.
281. R. Weisskoff, D. Chesler, J. Boxerman, and B. Rosen. Pitfalls in MR measurement of tissue blood flow with intravascular tracers: Which mean transit time? Magnetic Resonance in Medicine, 29(10):553-558, 1993.
282. D. Whitley. Genetic algorithm tutorial. Statistics and Computing, 4(11):65-85, 1994.
283. N. Wilke, C. Simm, J. Zhang, J. Ellermann, X. Ya, H. Merkle, G. Path, H. Lüdemann, R. Bache, and K. Ugurbil. Contrast-enhanced first-pass myocardial perfusion imaging: Correlation between myocardial blood flow in dogs at rest and during hyperemia. Magn. Reson. Med., 29(6):485-497, 1993.
284. D. Willshaw and C. von der Malsburg. How patterned neural connections can be set up by self-organization. Proc. Royal Society London, ser. B, 194:431-445, 1976.
285. A. Wismüller, O. Lange, D. Dersch, G. Leinsinger, K. Hahn, B. Pütz, and D. Auer. Cluster analysis of biomedical image time-series. International Journal on Computer Vision, 46:102-128, 2 2002.
286. A. Wismüller, A. Meyer-Bäse, O. Lange, D. Auer, M. Reiser, and D. Sumners. Model-free fMRI analysis based on unsupervised clustering. Journal of Biomedical Informatics, 37(9):13-21, 2004.
287. K. Woods. Automated Image Analysis Techniques for Digital Mammography. PhD thesis, University of South Florida, 1994.
288. R. Woods, S. Cherry, and J. Mazziotta. Rapid automated algorithm for aligning and reslicing PET images. Journal of Computer Assisted Tomography, 16:620-633, 8 1992.
289. H. Yang and S. Amari. A stochastic natural gradient descent algorithm for blind signal separation. In S. S.Usui, Y. Tohkura and E.Wilson, editors, Proc. IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VI, pages 433-442, 1996.
290. H. Yang and S. Amari. Adaptive on-line learning algorithms for blind separation-maximum entropy and minimum mutual information. Neural Computation, 9:1457-1482, 1997.
291. A. Yeredor. Blind source separation via the second characteristic function. Signal Processing, 80(5):897-902, 2000.
292. A. Yeredor. Non-orthogonal joint diagonalization in the leastsquares sense with application in blind source separation. IEEE Trans. on Signal Processing, 50(7):1545-1553, 2002.
293. E. Yousef, R. Duchesneau, and R. Alfidi. Magnetic resonance imaging of the breast. Radiology, 150(2):761-766, 1984.
294. S. Yu and L. Guan. A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Transactions on Medical Imaging, 19(8):115-126, 2000.
295. L. Zadeh. Fuzzy sets. Information and Control, 8(3):338-353, 1965.
296. A. Ziehe, M. Kawanabe, S. Harmeling, and K.-R. Müller. Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation. Journal of Machine Learning Research, 4:1319-1338, 2003.
297. A. Ziehe, P. Laskov, K.-R. Müller, and G. Nolte. A linear least-squares algorithm for joint diagonalization. In Proc. of ICA 2003, pages 469-474, 2003.
298. A. Ziehe and K.-R. Müller. TDSEP: An efficient algorithm for blind separation using time structure. In L. Niklasson, M. Bodén, and T. Ziemke, editors, Proc. of ICANN 98, pages 675-680. Springer Verlag, Berlin, 1998.
299. K. Zierler. Theoretical basis of indicator-dilution methods for measuring flow and volume. Circ. Res., 10(6):393-407, 1965.
300. A. Zijdenbos, B. Dawant, R. Margolin, and A. Palmer. Morphometric analysis of white matter lesions in MR images: Method and validation. IEEE Transactions on Medical Imaging, 13(12):716-724, 1994.
301. X. Zong, A. Meyer-Bäse, and A. Laine. Multiscale segmentation through a radial basis neural network. IEEE Int. Conf. on Image Processing, 3(8):400-403, 1997.