Machine learning for detection and diagnosis of disease

Annual Review of Biomedical Engineering

Volumn 8, Issue , 2006, Pages 537-565

  Sajda, Paul ^a  

^a Columbia University ^*  (United States)

Author keywords

Bayesian network; Blind source separation; Computational biology; Medical imaging; Support vector machine

Indexed keywords

ALGORITHMS; BIOMEDICAL ENGINEERING; BLIND SOURCE SEPARATION; DIAGNOSIS; LEARNING SYSTEMS;

ALGORITHMIC CONSTRUCTION; BAYESIAN NETWORKS; COMPUTATIONAL BIOLOGY; SUPPORT VECTOR MACHINES;

DISEASES;

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BAYES THEOREM; BIOMEDICINE; BRAIN CANCER; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC PROCEDURE; DIAGNOSTIC VALUE; MACHINE LEARNING; MAMMOGRAPHY; MAN MACHINE INTERACTION; METHODOLOGY; REVIEW; SUPPORT VECTOR MACHINE;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS, CLINICAL; DIAGNOSIS, COMPUTER-ASSISTED; GENE EXPRESSION PROFILING; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; TUMOR MARKERS, BIOLOGICAL;

EID: 33748181096     PISSN: 15239829     EISSN: None     Source Type: Book Series    
DOI: 10.1146/annurev.bioeng.8.061505.095802     Document Type: Review

References (128)

1. Nishikawa RM, Haldemann R, Giger M, Wolverton D, Schmidt R, Doi K. 1995. Performance of a computerized detection scheme for clustered microcalcifications on a clinical mammography workstation for computer-aided diagnosis. Presented at Radiol. Soc. North Am., p. 425. Chicago, IL (Abstr.)
2. Nishikawa RM, Schmidt RA, Osnis RB, Giger ML, Doi K, Wolverton DE. 1996. Two-year evaluation of a prototype clinical mammographic workstation for computer-aided diagnosis. Radiology 201(P):256
3. Bishop CM. 1995. Neural Networks for Pattern Recognition. New York: Oxford Univ. Press
4. Shortliffe EH, Buchanan B. 1975. A model of inexact reasoning in medicine. Math. Biosci. 23:351-79
5. Miller RA, Pople HE, Myers JD. 1982. Internist-1: an experimental computer-based diagnostic consultant for general internal medicine. N. Engl. J. Med. 307:468-76
6. Jolliffe IT. 1989. Principal Component Analysis. New York: Springer-Verlag
7. Lee DD, Seung HS. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401:788-91
8. Ochs MF, Stoyanova RS, Arias-Mendoza F, Brown TR. 1999. A new method for spectral decomposition using a bilinear Bayesian approach. J. Magn. Reson. 137:161-76
9. Parra L, Spence C, Ziehe A, Müller KR, Sajda P. 2000. Unmixing hyperspectral data. In Advances in Neural Information Processing Systems 12, ed. SA Solla, TK Leen, K-R Muller, pp. 942-48. Cambridge, MA: MIT Press
10. Plumbley M. 2002. Conditions for non-negative independent component analysis. IEEE Signal Proc. Lett. 9:177-80
11. Sajda P, Du S, Parra L, Stoyanova R, Brown T. 2003. Recovery of constituent spectra in 3D chemical shift imaging using non-negative matrix factorization. Proc. Int. Symp. Ind. Component Anal. Blind Signal Separation, 4th, Nara, Jpn, pp. 71-76
12. Sajda P, Du S, Brown TR, Stoyanova R, Shungu DC, et al. 2004. Non-negative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain. IEEE Trans. Med. Imaging 23(12):1453-653
13. Kao KC, Yang YL, Boscolo R, Sabatti C, Roychowdhury V, Liao JC. 2004. Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis. Proc. Natl. Acad. Sci. USA 101(2):641-46
14. Liao JC, Boscolo R, Yang YL, Tran LM, Sabatti C, Roychowdhury VP. 2003. Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl. Acad. Sci. USA 100(26):15522-27
15. Parra L, Sajda P. 2003. Blind source separation via generalized eigenvalue decomposition. J. Machine Learn. Res. Spec. Iss. ICA 4:1261-69
16. Parra L, Spence C. 2000. Convolutive blind source separation of non-stationary sources. IEEE Trans. Speech Audio Proc. May: 320-27
17. Sajda P, Zeevi YY, eds. 2005. Blind Source Separation and Deconvolution in Imaging and Image Processing, Vol. 15, Int. J. Imaging Syst. Technol. New York: Wiley Intersci.
18. Hyvärinen A, Karhunen J, Oja E. 2001. Independent Component Analysis. New York: Wiley Intersci.
19. Pearlmutter B, Parra LC. 1995. Maximum likelihood source separation: a context-sensitive generalization of ICA. In Advances in Neural Information Processing Systems, ed. MC Mozer, MI Jordan, T Petsche, Vol. 9. Cambridge, MA: MIT Press
20. Bell AJ, Sejnowski TJ. 1995. An information-maximization approach to blind separation and blind deconvolution. Neural Comp. 7:1129-59
21. Comon P. 1994. Independent component analysis, a new concept? Signal Proc. 36(3):287-314
22. Hojen-Sorensen P, Winther O, Hansen LK. 2002. Mean-field approaches to independent component analysis. Neural Comp. 14:889-918
23. Molgedey L, Schuster HG. 1994. Separation of a mixture of independent signals using time delayed correlations. Phys. Rev. Lett. 72(23):3634-37
24. Cardoso J-F, Souloumiac A. 1993. Blind beamforming for non Gaussian signals. IEEE Proc. F 140(6):362-70
25. Belouchrani A, Abed-Meraim K, Cardoso J-F, Moulines E. 1997. A blind source separation technique using second-order statistics. IEEE Trans. Signal Proc. 45:434-44
26. Ramoser H, Mueller-Gerking J, Pfurtscheller G. 2000. Optimal spatial filtering of single-trial EEG during imagined hand movements. IEEE Trans. Rehab. Eng. 8(4):441-46
27. Wainwright MJ, Simoncelli EP. 1999. Scale mixtures of Gaussians and the statistics of natural images. In Advances in Neural Information Processing Systems, ed. SA Solla, TK Leen, K-R Müller, 12:855-61. Cambridge, MA: MIT Press
28. Parra LC, Spence CD, Sajda P. 2000. Higher-order statistical properties arising from the non-stationarity of natural signals. In Advances in Neural Information Processing Systems, pp. 786-92. Cambridge, MA: MIT Press
29. Lee DD, Seung HS. 2001. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems 13, pp. 556-562. Cambridge, MA: MIT Press
30. Lee DD, Seung HS. 1997. Unsupervised learning by convex and conic coding. In Advances in Neural Information Processing Systems, 9:515-21. Cambridge, MA: MIT Press
31. Hoyer PO. 2002. Non-negative sparse coding. In Neural Networks for Signal Processing XII (Proc. IEEE Workshop on Neural Networks for Signal Processing, Martigny, Switzerland), pp. 557-65
32. Guillamet D, Bressan M, Vitria J. 2001. A weighted non-negative matrix factorization for local representations. IEEE Comput. Soc. Conf. Vision Pattern Recog., pages 942-47
33. Guillamet D, Schiele, and J Vitria. Analyzing non-negative matrix factorization for image classification. In Pattern Recognition, 2002. Proceedings. 16th International Conference on, volume 2, pages 116-119, 2002.
34. W Xu, X Liu, and Y Gong. Document clustering based on non-negative matrix factorization. In SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 267-273, New York, NY, USA, 2003. ACM Press.
35. Wang B, Plumbley MD. 2005. Musical audio stream separation by non-negative matrix factorization. Proc. DMRN Summer Conf., Glasgow, Scotland
36. Lee JS, Lee DD, Choi S, Lee DS. 2001. Application of non-negative matrix factorization to dynamic positron emission tomography. Int. Conf. Ind. Component Anal. Blind Signal Separation, 3rd, San Diego, ed. T-W Lee, T-P Jung, S Makeig, TJ Sejnowski, pp. 629-32
37. Brunei JP, Tamayo P, Golub TR, Mesirov JP. 2004. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. USA 101(12):4164-69
38. Inamura K, Fujiwara T, Hoshida Y, Isagawa T, Jones MH, et al. 2005. Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and non-negative matrix factorization. Oncogene 24(47):7105-13
39. Negendank WG, Sauter R, Brown TR, Evelhoch JL, Falini A, et al. 1996. Proton magnetic resonance spectroscopy in patients with glial tumors: a multicenter study. J. Neurosurg. 84:449-58
40. Edelenyi FS, Rubin C, Estève F, Grand S, Décorps M, et al. 2000. A new approach for analyzing proton magnetic resonance spectroscopic images of brain tumors: nosologic images. Nat. Med. 6:1287-89
41. 1H-chemical shift imaging method. NMR Biomed. 10:25-30
42. Mierisova S, Ala-Korpela M. 2001. MR spectroscopy quantitation: a review of frequency domain methods. NMR Biomed. 14(4):247-59
43. Vanhamme L, Huffel SV, Hecke PV, Ormondt DV. 1999. Time domain quantification of series of Biomed. magnetic resonance spectroscopy signals. J. Magn. Reson. 140:120-30
44. Ladroue C, Howe FA, Griffiths JR, Tate AR. 2003. Independent component analysis for automated decomposition of in vivo magnetic resonance spectra. Magn. Reson. Med. 50:697-703
45. Nuzillard D, Bourg S, Nuzillard J-M. 1998. Model-free analysis of mixtures by NMR using blind source separation. J. Magn. Reson. 133:358-63
46. Nicholson JK, Lindon JC, Holmes E. 1999. Metabonomics: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological nmr spectroscopic data. Xenobiotica 29(11):1181-89
47. Keun HC, Ebbels TM, Antti H, Bollard ME, Beckonert O, et al. 2002. Analytical reproducibility in 1H NMR-based metabonomic urinalysis. Chem. Res. Toxicol. 15(11):1380-86
48. Beckwith-Hall BM, Nicholson JK, Nicholls AW, Foxall PJD, Lindon JC, et al. 1998. Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chem. Res. Toxicol. 11(4):260-72
49. Robertson DG, Reily MD, Sigler RE, Wells DF, Paterson DA, Braden TK. 2000. Metabonomics: evaluation of nuclear magnetic resonance (NMR) and pattern recognition technology for rapid in vivo screening of liver and kidney toxicants. Toxicol. Sci. 57(2):326-37
50. Stoyanova R, Nicholls AW, Nicholson JK, Lindon JC, Brown TR. 2004. Automatic alignment of individual peaks in large high-resolution spectral data sets. J. Magn. Reson. 170(2):329-35
51. Baumgartner C, Böhm C, Baumgartner D. 2005. Modelling of classification rules on metabolic patterns including machine learning and expert knowledge. J. Biomed. Inform. 38(2):89-98
52. Nicholls AW, Holmes E, Lindon JC, Shockcor JP, Farrant RD, et al. 2001. Metabonomic investigations into hydrazine toxicity in the rat. Chem. Res. Toxicol. 14(8):975-87
53. Stoyanova R, Nicholson JK, Lindon JC, Brown TR. 2004. Sample classification based on bayesian spectral decomposition of metabonomic NMR data sets. Anal. Chem. 76(13):3666-74
54. Duda R, Hart P, Stork D. 2001. Pattern Classification. New York: Wiley. 2nd ed.
55. Burges CJC. 1998. A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discov. 2(2):121-67
56. Cristianni N, Shawe-Taylor J. 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, UK: Cambridge Univ. Press
57. Scholkopf B, Burges CJC, Smola AJ. 1998. Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press
58. Aizerman M, Braverman E, Rozonoer L. 1964. Theoretical foundations of the potential function method in pattern recognition learning. Automation Remote Contr. 25:821-37
59. Müller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B. 2001. An introduction to kernel-based learning algorithms 12. IEEE Neural Networks 12(2):181-201
60. Scholkopf B, Smola AJ. 2002. Learning with Kernels. Cambridge, MA: MIT Press
61. http://www.kernelmachines.org.
62. Vapnik V. 1999. The Nature of Statistical Learning Theory. Berlin: Springer-Verlag
63. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273-97
64. Kressel U. 1999. Pairwise classification and support vector machines. In Advances in Kernel Methods: Support Vector Learning, Chpt. 15. Cambridge, MA: MIT Press
65. Weston J, Watkins C. 1999. Support vector machines for multi-class pattern recognition. Proc. Eur. Symp. Artif. Neural Networks (ESANN 99), 7th, Bruges
66. Platt JC, Cristianini N, Shawe-Taylor J. 2000. Large margin dags for multiclass classification. In Advances in Neural Information Processing Systems, Vol. 12, pp. 547-53. Cambridge, MA: MIT Press
67. Crammer K, Singer Y. 2000. On the learnability and design of output codes for multiclass problems. Proc. Annu. Conf. Comp. Learn. Theory (COLT 2000), Standford Univ., Palo Alto, CA, June 28-July 1
68. Hsu C-W, Lin C-J. 2002. A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Networks 13:415-25
69. Scholkopf B, Smola AJ, Williamson RC, Bartlett PL. 2000. New support vector algorithms. Neural Comp. 12:1083-121
70. Majumder SK, Ghosh N, Gupta PK. 2005. Support vector machine for optical diagnosis of cancer. J. Biomed. Optics 10(2):024034
71. Gokturk SB, Tomasi C, Acar B, Beaulieu CF, Paik DS, et al. 2001. A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography. IEEE Trans. Med. Imaging 20(12):1251-60
72. El-Naqa I, Yang Y, Wernick MN, Galatsanos NP, Nishikawa RM. 2002. A support vector machine approach for detection of microcalcifications. IEEE Trans. Med. Imaging 21(12):1552-61
73. Wei L, Yang Y, Nishikawa RM, Jiang Y. 2005. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans. Med. Imaging 24(3):371-80
74. Kapetanovic IM, Rosenfeld S, Izmirlian G. 2004. Overview of commonly used bioinformatics methods and their applications. Ann. N.Y. Acad. Sci. 1020:10-21
75. Scholkopf B, Tsuda K, Vert J-P. 2004. Kernel Methods in Computational Biology. Cambridge, MA: MIT Press
76. Mukherjee S, Tamayo P, Slonim D, Verri A, Golub T, et al. 1999. Support vector machine classification of microarray data. Tech. Rep. AI Memo 1677, Mass. Inst. Technol., Cambridge, MA
77. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, et al. 1999. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531-37
78. Moler EJ, Chow ML, Mian IS. 2000. Analysis of molecular profile data using generative and discriminative methods. Physiol. Genomics 4:109-26
79. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. 2000. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10):906-14
80. Liu Y. 2004. Active learning with support vector machine applied to gene expression data for cancer classification. J. Chem. Inf. Comput. Sci. 44(6):1936-41
81. Segal NH, Pavlidis P, Antonescu CR, Maki RG, Noble WS, et al. 2003. Classification and subtype prediction of adult soft tissue sarcoma by functional genomics. Am. J. Pathol. 163(2):691-700
82. Segal NH, Pavlidis P, Noble WS, Antonescu CR, Viale A, et al. 2003. Classification of clear-cell sarcoma as a subtype of melanoma by genomic profiling. J. Clin. Oncol. 21(9):1775-81
83. Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S. 2005. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21(5):631-43
84. Rao RPN, Olshausen B, Lewicki MS, eds. 2002. Probabilistic Models of the Brain. Cambridge, MA: MIT Press
85. Ng AY, Jordan MI. 2002. On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. In Advances in Neural Information Processing Systems, ed. TG Dietterich, S Becker, Z Ghahramani, 14:841-48. Cambridge, MA, MIT Press
86. Jebara T. 2003. Machine Learning: Discriminative and Generative. Norwell, MA: Springer
87. Cover TM, Thomas JA. 1991. Elements of Information Theory. New York: Wiley
88. Romberg JK, Coi H, Baraniuk RG. 2001. Bayesian tree-stuctured image modeling using wavelet domain hidden markov models. IEEE Trans. Image Proc. 10(7):1056-68
89. Smyth P. 1997. Belief networks, hidden markov models, and markov random fields: a unifying view. Patt. Recogn. Lett. 18(11-13):1261-68
90. Jordan MI. 2004. Graphical models. Stat. Sci. (Spec. Iss. Bayesian Stat.), 19:140-55
91. Pearl J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco: Morgan Kaufmann
92. Yedidia JS, Freeman WT, Weiss Y. 2003. Understanding belief propagation and its generalizations. In Exploring Artificial Intelligence in the New Millennium, ed. G Lakemeyer, B Nebel, pp. 239-69. San Francisco: Morgan Kaufmann
93. Freeman WT, Pasztor EC, Carmichael OT. 2000. Learning low-level vision. Int. J. Comput. Vision 40:25-47
94. Ghahramani Z. 1998. Learning dynamic bayesian networks. In Adaptive Processing of Sequences and Data Structures. Lecture Notes in Artificial Intelligence, ed. CL Giles, M Gori, pp. 168-97. Berlin: Springer-Verlag
95. Rabiner L. 1989. A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 77(2):257-85
96. Andreassen S, Woldbye M, Falck B, Andersen SK. 1987. MUNIN-A causal probabilistic network for interpretation of electromyrographic findings. Proc. Int. Joint Conf. Artif. Intell., 10th, ed. J McDermott, pp. 366-72, Los Altos, CA: Morgan Kaufmann
97. Heckerman DE, Nathwani BN. 1992. An evaluation of the diagnostic accuracy of Pathfinder. Comput. Biomed. Res. 25(1):56-74
98. Diez FJ, Mira J, Iturralde E, Zubillaga S. 1997. DIVAVAL, a Bayesian expert system for echocardiography. Artif. Intell. Med. 10:59-73
99. Friedman N. 2004. Inferring cellular networks using probabilistic graphical models. Science 303:799-805
100. Nikovski D. 2000. Constructing bayesian networks for medical diagnosis from incomplete and partially correct statistics. IEEE Trans. Knowledge Data Eng. 12(4):509-16
101. Doi K, Giger ML, Nishikawa RM, Hoffmann K, MacMahon H, et al. 1993. Digital radiography: a useful clinical tool for computer-aided diagnosis by quantitative analysis of radiographic images. Acta Radiol. 34:426-39
102. Bird RE. 1990. Professional quality assurance for mammography screening programs. Radiology 177:8-10
103. Metz CE, Shen JH. 1992. Gains in accuracy from replicated readings of diagnostic images: prediction and assessment in terms of roc analysis. Med. Decision Making 12:60-75
104. Thurfjell EL, Lernevall KA, Taube AS. 1994. Benefit of independent double reading in a population-based mammography screening program. Radiology 191:241-44
105. Giger ML, Huo Z, Kupinski MA, Vyborny CJ. 2000. Computer-aided diagnosis in mammography. In Handbook of Medical Imaging; Volume 2. Medical Image Processing and Analysis, ed. M Sonka, JM Fitzpatrick, pp. 917-86. Bellingham, WA: SPIE Press
106. Floyd CE, Lo JY, Yun AJ, Sullivan DC, Kornguth PJ. 1994. Prediction of breast cancer malignancy using an artificial neural network. Cancer 74:2944-48
107. Jiang Y, Nishikawa RM, Wolverton DE, Metz CE, Giger ML, et al. 1996. Automated feature analysis and classification of malignant and benign microcalcifications. Radiology 198:671-78
108. Zheng B, Qian W, Clarke LP. 1996. Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications. IEEE Trans. Med. Imaging 15(5):589-97
109. Huo Z, Giger ML, Vyborny CJ, Wolverton DE, Schmidt RA, Doi K. 1998. Automated computerized classification of malignant and benign mass lesions on digital mammograms. Acad. Radiol. 5:155-68
110. Zhang W, Doi K, Giger ML, Wu Y, Nishikawa RM, Schmidt R. 1994. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Med. Phys. 21(4):517-24
111. Lo SC, Chan HP, Lin JS, Li H, Freedman MT, Mun SK. 1995. Artificial convolution neural network for medical image pattern recognition. Neural Networks 8(7/8):1201-14
112. Zhang W, Doi K, Giger ML, Nishikawa RM, Schmidt RA. 1996. An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. Med. Phys. 23:595-601
113. Lo JY, Kim J, Baker JA, Floyd CE. 1996. Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features. In Medical Imaging 1996: Image Processing, ed. MH Loew, 2710:725-32. Bellingham, WA: SPIE Press
114. Sajda P, Spence C, Pearson J, Nishikawa R. 1996. Integrating multi-resolution and contextual information for improved microcalcification detection. Digital Mammography 96:291-96
115. Chan HP, Sahiner B, Lam KL, Petrick N, Helvie MA, et al. 1998. Computerized analysis of mammographic microcalcifications in morphological and feature spaces. Med. Phys. 25:2007-19
116. Sajda P, Spence C. 1998. Applications of multi-resolution neural networks to mammography. In Advances in Neural Information Processing Systems, ed. MJ Kearns, SA Solla, DA Cohn, 11:938-44. Cambridge, MA: MIT Press
117. Sajda P, Spence C, Pearson J. 2002. Learning contextual relationships in mammograms using a hierarchical pyramid neural network. IEEE Trans. Med. Imaging 21(3):239-50
118. Sajda P, Spence C, Parra L. 2003. A multi-scale probabilistic network model for detection, synthesis and compression in mammographic image analysis. Med. Image Anal. 7(2):187-204
119. Li L, Qian W, Clarke LP. 1997. Digital mammography: computer-assisted diagnosis method for mass detection with multiorientation and multiresolution wavelet transforms. Acad. Radiol. 11(4):724-31
120. Netsch T, Peitgen HO. 1999. Scale-space signatures for the detection of clustered microcalcifications in digital mammograms. IEEE Trans. Med. Imaging 18(9):774-86
121. Sajda P, Laine A, Zeevi Y. 2002. Multi-resolution and wavelet representations for identifying signatures of disease. Dis. Markers 18(5-6):339-63
122. Dempster NM, Laird AP, Rubin DB. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B 39:185-97
123. Crouse MS, Nowak RD, Baraniuk RG. 1998. Wavelet-based statistical signal processing using hidden markov models. IEEE Trans. Signal Proc. 46(4):886-902
124. Cheng H, Bouman CA. 2001. Multiscale Bayesian segmentation using a trainable context model. IEEE Trans. Image Proc. 10(4):511-25
125. Coi H, Baraniuk RG. 2001. Multiscale image segmentation using wavelet-domain hidden markov models. IEEE Trans. Image Proc. 10(9):1309-21
126. Wainwright MJ, Simoncelli EP, Willsky AS. 2001. Random cascades on wavelet trees and their use in analyzing and modeling natural images. Appl. Comp. Harmonic Anal. 11:89-123
127. Wells WM, Viola P, Atsumi H, Nakajima S, Kikinis R. 1996. Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1):35-51
128. Hawkins J, Blakeslee S. 2004. On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines. Bellingham, WA: Henry Holt