Machine learning in medical imaging

IEEE Signal Processing Magazine

Volumn 27, Issue 4, 2010, Pages 25-38

  Wernick, Miles ^a   Yang, Yongyi ^b   Brankov, Jovan ^c   Yourganov, Grigori ^d   Strother, Stephen ^e  

^a ILLINOIS INSTITUTE OF TECHNOLOGY   (United States)

^b BEIJING JIAOTONG UNIVERSITY   (China)

^c IIT   (United States)

^d YORK UNIVERSITY   (Canada)

^e UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHARACTER RECOGNITION; DECISION MAKING; MEDICAL IMAGING; OPTICAL CHARACTER RECOGNITION;

AUTOMATED DECISION MAKING; UNIFIED CONCEPTS;

LEARNING SYSTEMS;

EID: 85032751398     PISSN: 10535888     EISSN: None     Source Type: Journal    
DOI: 10.1109/MSP.2010.936730     Document Type: Conference Paper

References (50)

1. T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning. New York: Springer-Verlag, 2003.
2. B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press, 2001, p. 626.
3. M. N. Wernick, "Pattern classification by convex analysis," J. Opt. Soc. Amer. A, Opt. Image Sci., vol.8, pp. 1874-1880, 1991.
4. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
5. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol.1, pp. 211-244, Sept. 2001.
6. R. G. Baraniuk, E. J. Candès, R. Nowak, and M. Vitterli, "Compressive sampling," IEEE Signal Processing Mag., vol.21, no.2, pp. 12-13, Mar. 2008.
7. B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap. Boca Raton, FL: CRC, 1994.
8. B. Efron and R. Tibshirani, "Improvements on cross-validation: The.632+ bootstrap method," J. Amer. Statist. Assoc., vol.92, no.438, pp. 548-560, June 1997.
9. S. C. Strother, J. Anderson, L. K. Hansen, U. Kjems, R. Kustra, J. Sidtis, S. Frutiger, S. Muley, S. LaConte, and D. Rottenberg, "The quantitative evaluation of functional neuroimaging experiments: The NPAIRS data analysis framework," Neuroimage, vol.15, no.4, pp. 747-771, Apr. 2002. (Pubitemid 34852491)
10. Image-Processing Techniques for Tumor Detection. New York: Marcel Dekker, 2002
11. Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer. Bellingham, WA: SPIE, 2006.
12. J. Tang, R. M. Rangayyan, J. Xu, I. El Naqa, and Y. Yang, "Computer-aided detection and diagnosis of breast cancer with mammography: recent advances," IEEE Trans. Inform. Technol. Biomed., vol.13, no.2, pp. 236-251, Mar. 2009.
13. I. El-Naqa, Y. Yang, M. N. Wernick, N. P. Galatsanos, and R. M. Nishikawa, "A support vector machine approach for detection of microcalcifications," IEEE Trans. Med. Imaging, vol.21, no.12, pp. 1552-1563, Dec. 2002.
14. L. Wei, Y. Yang, R. M. Nishikawa, M. N. Wernick, and A. Edwards, "Relevance vector machine for automatic detection of clustered microcalcifications," IEEE Trans. Med. Imaging, vol.24, no.10, pp. 1278-1285, Oct. 2005. (Pubitemid 41463566)
15. Y. Jiang, R. M. Nishikawa, D. E. Wolverton, C. E. Metz, M. L. Giger, R. A. Schmidt, C. J. Vyborny, and K. Doi, "Malignant and benign clustered microcalcifi-cations: automated feature analysis and classification," Radiology, vol.198, no.3, pp. 671-678, Mar. 1996.
16. L. Wei, Y. Yang, R. M. Nishikawa and Y. Jiang, "A study on several machine-learning methods for classification of malignant and benign clustered microcalcifi-cations," IEEE Trans. Med. Imaging, vol.24, no.3, pp. 371-380, Mar. 2005.
17. I. El-Naqa, Y. Yang, N. P. Galatsanos, R. M. Nishikawa, and M. N. Wernick, "A similarity learning approach to content-based image retrieval: application to digital mammography," IEEE Trans. Med. Imaging, vol.23, no.10, pp. 1233-1244, Oct. 2004.
18. L. Y. Wei, Y. Y. Yang, M. N. Wernick, and R. M. Nishikawa, "Learning of perceptual similarity from expert readers for mammogram retrieval," IEEE J. Select. Topics Signal Processing, vol.3, no.1, pp. 53-61, Feb. 2009.
19. N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, "Image quality assessment based on a degradation model," IEEE Trans. Image Processing, vol.9, no.4, pp. 636-650, Apr. 2000.
20. L. B. Lusted, "Signal detectability and medical decision making," Science, vol.171, p p. 1217-1219, 19 71.
21. C. E. Metz, B. A. Herman, and J. H. Shen, "Maximum-likelihood estimation of ROC curves from continuously-distributed data," Stat. Med., vol.17, no.9, pp. 1033-1110 5 3, 199 8.
22. K. J. Myers and H. H. Barrett, "Addition of a channel mechanism to the ideal-observer model," J. Opt. Soc. Amer. A, Opt. Image Sci., vol.4, no.12, pp. 2447-2457, Dec. 1987.
23. J. G. Brankov, Y. Yang, L. Wei, I. El Naqa, and M. N. Wernick, "Learning a channelized observer for image quality assessment," IEEE Trans. Med. Imaging, vol.28, no.7, pp. 991-999, July 2009.
24. J. Kippenhan, W. Barker, S. Pascal, J. Nagel, amd R. Duara, "Evaluation of a neural network classifier for PET scans of normal and Alzheimers disease subjects," J. Nucl. Med., vol.33, pp. 1459-1467, 1992.
25. P. Bandettini, "Functional MRI today," Int. J. Psychophysiol., vol.63, no.2, pp. 138-145, Feb. 2007.
26. K. J. Friston, J. T. Ashburner, S. J. Kiebel, and T. E. Nichols, Statistical Parametric Mapping: The Analysis of Functional Brain Images. New York: Academic, 2006.
27. F. Pereira, T. Mitchell, and M. Botvinick, "Machine learning classifiers and fMRI: A tutorial overview," Neuroimage, vol.45, no.1 (Suppl.), pp. S199-S209, Mar. 2009.
28. L. K. Hansen, "Multivariate strategies in functional magnetic resonance imaging," Brain Lang., vol.102, no.2, pp. 186-191, Aug. 2007.
29. D. Eidelberg, "Metabolic brain networks in neurodegenerative disorders: A functional imaging approach," Trends Neurosci., vol.32, no.10, pp. 548-557, Oct. 2009.
30. M. D. Fox and M. E. Raichle, "Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging," Nat. Rev. Neurosci., vol.8, no.9, pp. 700-711, Sept. 2007.
31. A. R. McIntosh, W. K. Chau, and A. B. Protzner, "Spatiotemporal analysis of event-related fMRI data using partial least squares," Neuroimage, vol.23, no.2, pp. 764-775, Oct. 2004.
32. C. F. Beckmann, M. DeLuca, J. T. Devlin, and S. M. Smith, "Investigations into resting-state connectivity using independent component analysis," Philos. Trans. R. Soc. Lond. B Biol. Sci., vol.360, no.1457, pp. 1001-1013, May 2005.
33. N. M. Correa, T. Adali, Y.-O. Li, and V. D. Calhoun, "Canonical correlation analysis for data fusion and group inferences," IEEE Signal Processing Mag., vol.27, no.4, pp. 39-50, 2010.
34. K. E. Stephan, L. M. Harrison, S. J. Kiebel, O. David, W. D. Penny, and K. J. Friston, "Dynamic causal models of neural system dynamics: Current state and future extensions," J. Biosci., vol.32, no.1, pp. 129-144, Jan. 2007.
35. C. J. Honey, O. Sporns, L. Cammoun, X. Gigandet, J. P. Thiran, R. Meuli, and P. Hagmann, "Predicting human resting-state functional connectivity from structural connectivity," Proc. Nat. Acad. Sci. USA, vol.106, no.6, pp. 2035-2040, Feb. 2009.
36. S. C. Strother, "Evaluating fMRI preprocessing pipelines," IEEE Eng. Med. Biol. Mag., vol.25, no.2, pp. 27-41, Mar.-Apr. 2006.
37. N. Lange, S. C. Strother, J. R. Anderson, F. A. Nielsen, A. P. Holmes, T. Kolen-da, R. Savoy, and L. K. Hansen, "Plurality and resemblance in fMRI data analysis," Neuroimage, vol.10, no.3, part 1, pp. 282-303, Sept. 1999.
38. N. Morch, L. K. Hansen, S. C. Strother, C. Svarer, D. A. Rottenberg, B. Lautrup, R. Savoy, and O. B. Paulson, "Nonlinear versus linear models in functional neuroimaging: Learning curves and generalization crossover," in Information Processing in Medical Imaging (Lecture Notes in Computer Science), J. Duncan and I. Gindi, Eds. 1997, pp. 259-270.
39. A. S. Lukic, M. N. Wernick, and S. C. Strother, "An evaluation of methods for detecting brain activations from functional neuroimages," Artif. Intell. Med., vol.25, no.1, pp. 69-88, May 2002.
40. K. J. Worsley, J. Cao, T. Paus, M. Petrides, and A. C. Evans, "Applications of random field theory to functional connectivity," Hum. Brain Mapp., vol.6, no.5-6, pp. 364-367, 1998. (Pubitemid 28460593)
41. T. P. Minka, "Automatic choice of dimensionality for PCA," Cambridge, MA: MIT, Rep. 514, 2004.
42. C. F. Beckmann and S. M. Smith, "Probabilistic independent component analysis for functional magnetic resonance imaging," IEEE Trans. Med Imaging, vol.23, no.2, pp. 137-152, Feb. 2004.
43. J. Mourao-Miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter, "Classifying brain states and determining the discriminating activation patterns: Support vector machine on functional MRI data," Neuroimage, vol.28, no.4, pp. 980-995, Dec. 2005.
44. S. LaConte, S. Strother, V. Cherkassky, J. Anderson, and X. Hu, "Support vector machines for temporal classification of block design fMRI data," Neuroimage, vol.26, no.2, pp. 317-329, June 2005.
45. A. S. Lukic, M. N. Wernick, D. G. Tzikas, X. Chen, A. Likas, N. P. Galatsanos, Y. Yang, F. Zhao, and S. C. Strother, "Bayesian kernel methods for analysis of functional neuroimages," IEEE Trans. Med Imaging, vol.26, no.12, pp. 1613-1624, Dec. 2007.
46. D. R. Hardoon, J. Mourao-Miranda, M. Brammer, and J. Shawe-Taylor, "Un-supervised analysis of fMRI data using kernel canonical correlation," Neuroimage, vol.37, no.4, pp. 1250-1259, Oct. 2007.
47. S. C. Strother, S. La Conte, L. Kai Hansen, J. Anderson, J. Zhang, S. Pu-lapura, and D. Rottenberg, "Optimizing the fMRI data-processing pipeline using prediction and reproducibility performance metrics: I. A preliminary group analysis," Neuroimage, vol.23 (Suppl. 1), pp. S196-S207, 2004.
48. J. Zhang, J. R. Anderson, L. Liang, S. K. Pulapura, L. Gatewood, D. A. Rot-tenberg, and S. C. Strother, "Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA," Magn. Reson. Imaging, vol.27, no.2, pp. 264-278, Feb. 2009.
49. J. Zhang, L. Liang, J. R. Anderson, L. Gatewood, D. A. Rottenberg, and S. C. Strother, "Evaluation and comparison of GLM-and CVA-based fMRI processing pipelines with Java-based fMRI processing pipeline evaluation system," Neuroim-age, vol.41, pp. 1242-1252, July 2008.
50. R. Kustra and S. C. Strother, "Penalized discriminant analysis of water PET brain images with prediction error selection of smoothness and regulariza-tion hyperparameters," IEEE Trans. Med. Imaging, vol.20, no.5, pp. 376-387, May 2001.