Skip to main content Accessibility help
×
Hostname: page-component-77c89778f8-m42fx Total loading time: 0 Render date: 2024-07-17T12:22:57.584Z Has data issue: false hasContentIssue false

Bibliography

Published online by Cambridge University Press:  28 May 2021

Michael Insana
Affiliation:
University of Illinois, Urbana-Champaign
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abbey, CK, Zemp, RJ, Liu, J, Lindfors, KK, Insana, MF, “Observer efficiency in discrimination tasks simulating malignant and benign breast lesions with ultrasound,” IEEE Trans. Med. Imaging, 25(2):198209, 2006.Google Scholar
Aggarwal, CC, Reddy, CK, eds., Data Clustering: Algorithms and Applications, CRC Press, Boca Raton, FL, 2014.CrossRefGoogle Scholar
Alvarez, RE, Macovski, A, “Energy-selective reconstructions in X-ray computerised tomography,” Phys. Med. Biol., 21(5):733744, 1976.Google Scholar
Anderson, TW, An Introduction to Multivariate Statistical Analysis, John Wiley, New York, 1958.Google Scholar
Andrews, HC, Hunt, BR, Digital Image Processing, Prentice-Hall, Engelwood Cliffs, NJ, 1977.Google Scholar
Arthur, D, Vassilvitskii, S, “K-means++: the advantages of careful seeding,” SODA 07: Proc. 18th Annual ACM-SIAM Symp. Discrete Algor., 1027–1035, 2007.Google Scholar
Assländer, J, Cloos, MA, Knoll, F, Sodickson, DK, Hennig, J, Lattanzi, R, “Low rank alternating direction method of multipliers reconstruction for MR fingerprinting,” Magn. Reson. Med., 79(1):8396, 2018.CrossRefGoogle ScholarPubMed
Baraniuk, RG, “Compressive sensing,” IEEE Sig. Proc. Mag., 24(4):118124, 2007.Google Scholar
Barrett, HH, “Objective assessment of image quality: effects of quantum noise and object variability,” J. Opt. Soc. Am. A, 7(7):12661278, 1990.CrossRefGoogle ScholarPubMed
Barrett, HH, Myers, KJ, Foundations of Image Science, Wiley-Interscience, Hoboken, NJ, 2004.Google Scholar
Bartle, RG, The Elements of Integration and Lebesgue Measure, Wiley Classics Library, vol. 56, Wiley, New York, 1995.CrossRefGoogle Scholar
Beck, A, Teboulle, M, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci., 2(1):183202, 2009.CrossRefGoogle Scholar
Bendat, JS, Piersol, AG, Random Data: Analysis and Measurement Procedures, 2nd ed., Wiley-Interscience, New York, 1986.Google Scholar
Ben-Naim, A, A Farewell to Entropy: Statistical Thermodynamics Based on Information, World Scientific, Singapore, 2008.CrossRefGoogle Scholar
Bevington, PR, Robinson, DK, Data Reduction and Error Analysis for the Physical Sciences, 2nd ed., WCB/McGraw-Hill, Boston, 1992.Google Scholar
Birgin, EG, Martinez, JM, Practical Augmented Lagrangian Methods for Constrained Optimization: Fundamentals of Algorithms, SIAM, Philadelphia, 2014.Google Scholar
Bishop, CM, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, UK, 1995.Google Scholar
Born, M, Wolf, E, Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light, 7th ed., Cambridge University Press, Cambridge, UK, 1999.Google Scholar
Box, GEP, “Science and statistics,” J. Am. Stat. Assoc., 71(356):791799, 1976.CrossRefGoogle Scholar
Boyd, S, Parikh, N, Chu, E, Peleato, B, Eckstein, J, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. Trends Mach. Learn., 3(1):1122, 2010.Google Scholar
Bracewell, RN, The Fourier Transform and Its Applications, 2nd ed., McGraw-Hill, New York, 1978.Google Scholar
Bruce, EN, Biomedical Signal Processing and Signal Modeling, Wiley & Sons, New York, 2001.Google Scholar
Bushberg, JT, Seibert, JA, Leibholdt, EM Jr, Boone, JM, The Essential Physics of Medical Imaging, 3rd ed., Lippincott Williams & Wilkins, Philadelphia, 2012.Google Scholar
Candès, E, Plan, Y, “Near-ideal model selection by 1 minimization,” Ann. Stat., 37(5A):21452177, 2009.CrossRefGoogle Scholar
Candès, EJ, Wakin, MB, “An introduction to compressive sampling,” IEEE Signal Proc. Mag., 25(2):2130, 2008.Google Scholar
Coleman, GB, Andrews, HC, “Image segmentation by clustering,” Proc. IEEE, 67(5):773– 785, 1979.Google Scholar
Cover, TM, “Learning in pattern recognition.” In: Methodologies of Pattern Recognition, S Watanabe, ed., Academic Press, New York, 1969.CrossRefGoogle Scholar
Cover, TM, Thomas, JA, Elements of Information Theory, John Wiley & Sons, New York, 1991.Google Scholar
Donoho, D, “Compressed sensing,” IEEE Trans. Inf. Theory, 52(4):12891306, 2006.CrossRefGoogle Scholar
Dorfman, DD, Alf, E Jr, “Maximum likelihood estimation of parameters of signal detection theory: a direct solution,” Psychometrika, 33(1):117124, 1968.Google Scholar
Duda, RO, Hart, PE, Stark, DG, Pattern Classification, 2nd ed., Wiley Interscience, John Wiley and Sons, New York, 2000.Google Scholar
Eldar, YC, G Kutyniok, Compressed Sensing: Theory and Applications, Cambridge University Press, Cambridge, UK, 2012.Google Scholar
Elshik, E, Bester, CR, Nel, A, “Appropriate solar spectrum usage: the novel design of a photovoltaic thermal system,” 2016. www.researchgate.net/publication/299559828_Appropriate_Solar_Spectrum_Usage_The_Novel_Design_of_a_Photovoltaic_Thermal_SystemGoogle Scholar
Evans, RD, The Atomic Nucleus, McGraw-Hill, New York, 1955.Google Scholar
Frank, SA, “The common patterns of nature,” J. Evol. Biol., 22(8):15631585, 2009.Google Scholar
Fredenberg, E, “Spectral and dual-energy x-ray imaging for medical applications,” Nucl. Inst. Methods Phys. Res. A, 878:7487, 2018.Google Scholar
Fukunaga, K, Introduction to Statistical Pattern Recognition, Academic Press, San Diego, 1990.Google Scholar
Gabor, D, “Theory of communication: part 1. The analysis of information,” J. Inst. Elect. Eng. Part III, Radio Commun., 93:429–441, 1946.Google Scholar
Garra BS, BS, Insana, MF, Shawker, TH, Wagner, RF, Bradford, M, Russell, MA, “Quantitative ultrasonic detection and classification for diffuse liver disease: comparison with human observer performance,Invest. Radiol., 24:196203, 1989.Google Scholar
Ghaboussi, J, Insana, MF, Understanding Systems: A Grand Challenge for 21st Century Engineering, World Scientific, Singapore, 2018.Google Scholar
Golub, GH, VanLoan, CF, Matrix Computations, 4th ed., Johns Hopkins University Press, Baltimore, 2013.Google Scholar
Gonzalez, RC, Woods, RE, Digital Image Processing, Addison-Wesley, Reading, MA, 1992.Google Scholar
Goodman, JW, Introduction to Fourier Optics, McGraw-Hill, San Francisco, 1968.Google Scholar
Goodman, JW, Statistical Optics, Wiley Interscience, New York, 1985.Google Scholar
Gradshteyn, IS, Ryzhik, IM, Table of Integrals, Series, and Products, 5th ed., A Jeffrey, ed., Academic Press, San Diego, 1994.Google Scholar
Data originally provided by W. B. Gratzer, Med. Res. Council Labs, Holly Hill, London, and N. Kollias, Wellman Laboratories, Harvard Medical School, Boston. See https://en.wikipedia.org/wiki/Pulse_oximetry.Google Scholar
Green, DM, Swets, JA, Signal Detection Theory and Psychophysics, Wiley & Sons, New York, 1966.Google Scholar
Guerrero, FG, “A new look at the classical entropy of written English,” CoRR, 2009. http://arxiv.org/abs/0911.2284.Google Scholar
Haldar, JP, Hernando, D, Liang, ZP, “Compressed-sensing MRI with random encoding,” IEEE Trans. Med. Imaging, 30(4):893903, 2011.CrossRefGoogle ScholarPubMed
Hastie, T, Tibshirani, R, Friedman, J, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, 2009.Google Scholar
Heffernan, JM, Smith, RJ, LM Wahl, “Perspectives on the basic reproductive ratio,J. R. Soc. Interface, 2:281293, 2005.Google Scholar
Hethcote, HW, “The basic epidemiological models: models, expressions for R0 parameter estimation, and applications,” In: Mathematical Understanding of Infectious Disease Dynamics, S Ma, Y Xia, eds., World Scientific, Singapore, 2008, pp. 161.Google Scholar
Hoppensteadt, FC, Peskin, CS, Modeling and Simulation in Medicine and the Life Sciences, Springer, New York, 2002.Google Scholar
ICRU Report 54, Medical Imaging: The Assessment of Image Quality, 7910 Woodmont Avenue, Bethesda, MD, 20814, April 1996.Google Scholar
Insana, MF, Hall, TJ, “Visual detection efficiency in ultrasonic imaging: a framework for objective assessment of image quality,” J. Acoust. Soc. Am., 95(4):20812090, 1994.CrossRefGoogle Scholar
Insana, MF, Wagner, RF, Garra, BS, Momenan, R, Shawker, TH, “Pattern recognition methods for optimizing multivariate tissue signatures in diagnostic ultrasound,Ultrasonic Imag., 8:165180, 1986.Google Scholar
Jaroszeski, MJ, Radcliff, G, “Fundamentals of flow cytometry,” Molec. Biotech., 11:37– 53, 1999.Google Scholar
Kak, AC, Slaney, M, Principles of Computerized Tomographic Imaging, SIAM, Philadel-phia, 2001.CrossRefGoogle Scholar
Keeling, MJ, Rohani, P, Modeling Infectious Diseases in Humans and Animals, Princeton University Press, Princeton, NJ, 2007.Google Scholar
Kerner, EH, “Dynamical aspects of kinetics,” Bull. Math. Biophys. 26, 333–349, 1964; also EH Kerner, “Note on Hamiltonian format of Lotka–Volterra dynamics,” Phys. Lett. A, 151:401–402, 1990.Google Scholar
Kerner, EH, “Comment on Hamiltonian structures for the n-dimensional Lotka-Volterra equations,” J. Math. Phys. 38(2):12181223, 1997.CrossRefGoogle Scholar
Kim, M-W, Zhu, Y, Hedhli, J, Dobrucki, LW, Insana, MF, “Multi-dimensional clutter filter optimization for ultrasonic perfusion imaging,” IEEE Trans. Ultrason. Ferroelec. Freq. Control, 65(11):20202029, 2018.Google Scholar
Kullback, S, Information Theory and Statistics, Dover, New York, 1997.Google Scholar
Lancaster, JL, Hasegawa, B, Fundamental Mathematics and Physics of Medical Imaging, CRC Press, Boca Raton, FL, 2016.CrossRefGoogle Scholar
Lehmann, LA, Alvarez, RE, Macovski, A, Brody, WR, Pelc, NJ, Riederer, SJ, Hall, AL, “Generalized imaging combinations in dual kVp digital radiography,” Med. Phys., 8(5):659667, 1981.CrossRefGoogle ScholarPubMed
Lloyd, SP, “Least squares quantization in PCM,” IEEE Trans. Inf. Theory, 28(2):129– 137, 1982.Google Scholar
Macovski, A, “Ultrasonic imaging using arrays,” Proc. IEEE, 67:484495, 1979.Google Scholar
Macovski, A, Medical Imaging Systems, Prentice-Hall, Upper Saddle River, NJ, 1983.Google Scholar
Marple, SL, Jr, Digital Spectral Analysis with Applications, Prentice-Hall, Englewood Cliffs, NJ, 1987.Google Scholar
McDonough, RN, Whalen, AD, Detection of Signals in Noise, 2nd ed., Academic Press, San Diego, 1995.Google Scholar
Murray, JD, Mathematical Biology, 3rd ed., vol. 1, Springer, New York, 2007.Google Scholar
Nakamura, K, ed., Ultrasonic Transducers: Materials and Design for Sensors, Actuators and Medical Applications, Woodhead Publishing, Elsevier, 2012.Google Scholar
Nguyen, NQ, Abbey, CK, Insana, MF, “Objective assessment of sonographic quality I: task information,” IEEE Trans. Med. Imaging, 32(4):683690, 2013.Google Scholar
North, DO, “An analysis of the factors which determine signal/noise discrimination in pulse-carrier systems,” Tech. Rep. PTR-6C, RCA Laboratories, June 1943. Reprinted in Proc. IEEE, 51:1016–1027, 1963.Google Scholar
Obuchowski, NA, Katzman McClish, D, “Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices,Stat. Med., 16:15291542, 1997.Google Scholar
Odum, EP, Fundamentals of Ecology, W. B. Saunders, Philadelphia, 1953.Google Scholar
Oppenheim, AV, Schafer, RW, Buck, JR, Discrete-Time Signal Processing, 2nd ed., Prentice-Hall, Upper Saddle River, NJ, 1999.Google Scholar
Palsson, , Biology, Systems: Properties of Reconstructed Networks, Cambridge University Press, Cambridge UK, 2006.Google Scholar
Pan, X, Metz, CE, “The ‘proper’ binormal model: parametric receiver operating characteristic curve estimation with degenerate data,” Acad. Radiol., 4:380–389, 1997.Google Scholar
Papoulis, A, Probability, Random Variables, and Stochastic Processes, 3rd ed., WCB McGraw-Hill, Boston, 1991.Google Scholar
Penrose, R, “A generalized inverse for matrices,” Math. Proc. Cambridge Phil. Soc., 51(3):406413, 1955.Google Scholar
Plank, M, “Hamiltonian structures for the n-dimensional Lotka–Volterra equations,” J. Math. Phys. 36(7): 35203534, 1995.CrossRefGoogle Scholar
Prasad, SC, Hendee, WR, Carson, PL, “Intensity distribution, modulation transfer function, and the effective dimension of a line-focus x-ray focal spot,” Med. Phys., 3(4):217223, 1976.Google Scholar
Rajkomar, A, Dean, J, Kohane, I, “Machine learning in medicine,” N. Engl. J. Med., 380:1347–1358, 2019.CrossRefGoogle Scholar
Aditya, DP Rao, CH Renumadhavi, MG Chandra, R Srinivasan, “Compressed sensing methods for DNA microarrays, RNA interference, and metagenomics,J. Comput. Biol., 22(2):145158, 2015.Google Scholar
Ravishankar, S, Bresler, Y, “Learning sparsifying transforms,” IEEE Trans. Sig. Proc. 61(5):10721086, 2013.Google Scholar
Ravishankar, S, Ye, JC, Fessler, JA, “Image reconstruction: from sparsity to data-adaptive methods and machine learning,” Proc. IEEE, 108(1):310, 2020.Google Scholar
3rd Baron Rayleigh, JW Strutt, “The problem of the random walk,Nature, 72(1866):318, 1905.Google Scholar
Rodgers, JL, Nicewander, WA, Toothaker, L, “Linearly independent, orthogonal, and uncorrelated variables,” Am. Statistician, 38(2):133134, 1984.Google Scholar
Ross, S, A First Course in Probability, Macmillan, New York, 1976.Google Scholar
Rubinow, SI, Introduction to Mathematical Biology, John Wiley & Sons, New York, 1975.Google Scholar
Savageau, MA, Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology, CreateSpace Independent Publishing Platform, 2010.Google Scholar
Schade, O, Image Quality: A Comparison of Photographic and Television Systems, RCA Laboratories, Princeton, NJ, 1975.Google Scholar
Semmlow, JL, Circuits Systems and Signals for Bioengineers: A Matlab-Based Introduction, Elsevier, New York, 2005.Google Scholar
Shannon, CE, “A mathematical theory of communication,” Bell Syst. Tech. J., 27:379– 423, 623–656, 1948.CrossRefGoogle Scholar
Shannon, CE, “Prediction and entropy of printed English,” Bell Syst. Tech. J., 30:47–51, 1951.Google Scholar
Shapiro, HM, Practical Flow Cytometry, 4th ed., Wiley-Liss, Hoboken, NJ, 2003.CrossRefGoogle Scholar
Shaw, R, “The equivalent quantum efficiency of the photographic process,” J. Photographic Sci., 11:199–204, 1963.Google Scholar
Simon, W, Mathematical Techniques for Biology and Medicine, Dover, New York, 1986.Google Scholar
Slepian, D, Key Papers in the Development of Information Theory, IEEE Press, Piscat-away, NJ, 1974.Google Scholar
So, S, Paliwal, KK, “Reconstruction of a signal from the real part of its discrete Fourier transform,” IEEE Sig. Proc. Mag., 35(2):162–164, 174, 2018.Google Scholar
Sommer, FG, Hoppe, RT, Fellingham, L, Carroll, BA, Solomon, H, Yousem, S, “Spleen structure in Hodgkin disease: ultrasonic characterization: work in progress,” Radiology, 153(1):219222, 1984.Google Scholar
Statnikov, A, Aliferis, CF, Hardin, DP, Guyon, I, A Gentle Introduction to Support Vector Machines in Biomedicine, vol. 1, World Scientific, Singapore, 2011.Google Scholar
Tanner, WP, Jr, Birdsall, TG, “Definitions of d and η as psychophysical measures,” J. Acoust. Soc. Am., 30(10):922928, 1958.Google Scholar
Tharwat, A, “Linear vs. quadratic discriminant analysis classifier: a tutorial,” Int. J. Applied Pattern Recognition, 3(2):145180. 2016.Google Scholar
Thomenius, KE, “Evolution of ultrasound beamformers,” Proc. IEEE Ultrason. Symp., 1615–1622, 1996.Google Scholar
Tibshirani, R, “Regression shrinkage and selection via the lasso: a retrospective,” J. Royal Statist. Soc. B, 73(pt 3):273282, 2011.Google Scholar
Tward, DJ, Siewerdsen, JH, “Noise aliasing and the 3D NEQ of flat-panel cone-beam CT: effect of 2D/3D apertures and sampling,” Med. Phys., 36(8):38303843, 2009.Google Scholar
Urkowitz, H, Signal Theory and Random Processes, Artech House, Norwood, MA, 1983.Google Scholar
Van Trees, HL, Detection, Estimation, and Modulation Theory, Part I, Wiley, New York, 1968. Note that a second edition to Part I, published in 2013 by Wiley with coauthors Kristine L. Bell and Zhi Tian, expands the development of optimal detectors.Google Scholar
Van Trees, HL, Detection, Estimation, and Modulation Theory, Part IV: Optimum Array Processing, Wiley, New York, 2002.Google Scholar
Verhulst, F, Nonlinear Differential Equations and Dynamical Systems, Springer, Berlin, 1996.Google Scholar
Wagner, RF, Brown, DG, “Overview of a unified SNR analysis of medical imaging systems,” IEEE Trans. Med. Imaging, 1(4):210213, 1982.Google Scholar
Wagner, RF, Brown, DG, “Unified SNR analysis of medical imaging systems,Phys. Med. Biol., 30:489518, 1985.Google Scholar
Wagner, RF, Brown, DG, Pastel, MS, “Application of information theory to the assessment of computed tomography,” Med. Phys., 6(2):8394, 1979.Google Scholar
Wagner, RF, Insana, MF, Jennings, RJ, Brown, DG, “Multivariate signal and texture discrimination in medical imaging,” In: Statistical Efficiency of Natural and Artificial Vision, HG Barlow, DG Peli, eds., Rank Prize Fund Publishing, London, 1986.Google Scholar
Wagner, RF, Insana, MF, Smith, SW, “Fundamental correlation lengths of coherent speckle in medical ultrasonic images,IEEE Trans. Ultrason. Ferro. Freq. Control, 35:3444, 1988.Google Scholar
Wagner, RF, Weaver, KE, Denny, EW, Bostrom, RG, “Toward a unified view of radiological imaging systems. Part I: noiseless images,” Med. Phys., 1(1):1124, 1974.Google Scholar
Wernick, M, JN Aarsvold, Emission Tomography: The Fundamentals of PET and SPECT, Elsevier/Academic Press, San Diego, 2004.Google Scholar
Zadeh, LA,”The determination of the impulsive response of variable networks,” J. Appl. Phys., 21:642–645, 1950.Google Scholar
Zadeh, LA, “Initial conditions in linear-varying parameter systems,” J. Appl. Phys., 22(6):782786, 1951.Google Scholar
Zemp, RJ, Abbey, CK, Insana, MF, “Linear system models for ultrasonic imaging: application to signal statistics,IEEE Trans. Ultrason. Ferro. Freq. Control, 50:642654, 2003.Google Scholar
Zhou, DP, Peng, W, Chen, L, Bao, X, “Brillouin optical time-domain analysis via compressed sensing,” Opt. Lett., 43(22):54965499, 2018.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Bibliography
  • Michael Insana, University of Illinois, Urbana-Champaign
  • Book: Biomedical Measurement Systems and Data Science
  • Online publication: 28 May 2021
  • Chapter DOI: https://doi.org/10.1017/9781316831823.020
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Bibliography
  • Michael Insana, University of Illinois, Urbana-Champaign
  • Book: Biomedical Measurement Systems and Data Science
  • Online publication: 28 May 2021
  • Chapter DOI: https://doi.org/10.1017/9781316831823.020
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Bibliography
  • Michael Insana, University of Illinois, Urbana-Champaign
  • Book: Biomedical Measurement Systems and Data Science
  • Online publication: 28 May 2021
  • Chapter DOI: https://doi.org/10.1017/9781316831823.020
Available formats
×