Bibliography

[1] Rosenfeld, A. and Kak, A. C., Digital Image Processing, Academic Press, New York, 1982.

[2] Bajcsy, R. and Kovacic, S., Multiresolution elastic matching, Comp. Vision Graph. Image Proc., Vol. 46, pp. 1-21, 1989.

[3] Lim, K. O. and Pfefferbaum, A., Segmentation of MR brain images into cerebrospinal fluid spaces, white, and gray matter, J. Comput. Assist. Tomogr., Vol. 13, pp. 588-593, 1989.

[4] Brzakovic, D., Luo, X. M., and Brzakovic, P., An approach to automated detection of tumors in mammograms, IEEE Trans. Med. Imaging, Vol. 9, pp. 233-241, 1990.

[5] Liang, Z., MacFall, J. R., and Harrington, D. P., Parameter estimation and tissue segmentation from multispectral MR images, IEEE Trans. Med. Imaging, Vol. 13, pp. 441-449, 1994.

[6] Ardekani, B. A., Braun, M., Hutton, B. F., Kanno, I., and Iida, H., A fully automatic multimodality image registration algorithm, J. Comput. Assist. Tomogr., Vol. 19, pp. 615-623, 1995.

[7] Bankman, I. N., Nizialek, T., Simon, I., Gatewood, O. B., Weinberg, I. N., and Brody, W. R., Segmentation algorithms for detecting microcalcifications in mammograms, IEEE Trans. Inform. Technol. Biomed., Vol. 1, pp. 141-149, 1997.

[8] Small, G. W., Stern, C. E., Mandelkern, M. A., Fairbanks, L. A., Min, C. A., and Guze, B. H., Reliability of drawing regions of interest for positron emission tomographic data, Psych. Res., Vol. 45, pp. 177-185, 1992.

[9] White, D. R., Houston, A. S., Sampson, W. F., and Wilkins, G. P., Intra-and interoperator variations in region-of-interest drawing and their effect on the measurement of glomerular filtration rates, Clin. Nucl. Med., Vol. 24, pp. 177-181, 1999.

[10] Hoffman, E. J., Huang, S. C., and Phelps, M. E., Quantitation in positron emission computed tomography, 1: Effect of object size, J. Comput. Assist. Tomogr., Vol. 3, pp. 299-308, 1979.

[11] Mazziotta, J. C., Phelps, M. E., Plummer, D., and Kuhl, D. E., Quantitation in positron emission compted tomography, 5: Physical-anatomical effects, J. Cereb. Blood Flow Metab., Vol. 5, pp. 734-743, 1981.

[12] Hutchins, G. D., Caraher, J. M., and Raylman, R. R., Aregion of interest strategy for minimizing resolution distortions in quantitative myocardial PET studies, J. Nucl. Med., Vol. 33, pp. 1243-1250, 1992.

[13] Welch, A., Smith, A. M., and Gullberg, G. T., An investigation of the effect of finite system resolution and photon noise on the bias and precision of dynamic cardiac SPECT parameters, Med. Phys., Vol. 22, pp. 1829-1836, 1995.

[14] Bezdek, J., Hall, L., and Clarke, L., Review of MR image segmentation techniques using pattern recognition, Med. Phys., Vol. 20, pp. 10331048, 1993.

[15] Mazziotta, J. C. and Koslow, S. H., Assessment of goals and obstacles in data acquisition and analysis from emission tomography: Report of a series of international workshops, J. Cereb. Blood Flow Metab., Vol. 7(Suppl. 1), pp. S1-S31, 1987.

[16] Mazziotta, J. C., Pelizzari, C. A., Chen, G. T., Bookstein, F. L., and Valentino, D., Region of interest issues: The relationship between structure and function in the brain, J. Cereb. Blood Flow Metab., Vol. 11, pp. A51-A56, 1991.

[17] Fu, K. S. and Mui, J. K., A survey on image segmentation, Pattern Recogn., Vol. 13, pp. 3-16, 1981.

[18] Haralick, R. M. and Shapiro, L. G., Survey: Image segmentation techniques, Comput. Vision Graphics Image Proc., Vol. 29, pp. 100-132, 1985.

[19] Pal, N. R. and Pal, S. K., A review on image segmentation techniques, Pattern Recogn., Vol. 26, pp. 1227-1249, 1993.

[20] Gonzalez, R. C. and Woods, R. E., Digital Image Processing, Addison-Wesley, Reading, MA, 1993.

[21] Castleman, K. R., Digital Image Processing, Prentice Hall, Upper Saddle River, NJ, 1996.

[22] Kittler, J., Illingworth, J., and Foglein, J., Threshold based on a simple image statistics, Comp. Vision Graph. Image Proc., Vol. 30, pp. 125-147, 1985.

[23] Chow, C. K. and Kaneko, T., Automatic boundary detection of the left ventricle from cineangiograms, Comput. Biomed. Res., Vol. 5, pp. 388410, 1972.

[24] Marr, D. and Hildreth, E., Theory of edge detection, Proc. Roy. Soc. London, Vol. 27, pp. 187-217, 1980.

[25] Sun, Y., Lucariello, R. J., and Chiaramida, S. A., Directional low-pass filtering for improved accuracy and reproducibility of stenosis quantification in coronary arteriograms, IEEE Trans. Med. Imaging, Vol. 14, pp. 242-248, 1995.

[26] Faber, T. L., Akers, M. S., Peshock, R. M., and Corbett, J. R., Three-dimensional motion and perfusion quantification in gated singlephoton emission computed tomograms, J. Nucl. Med., Vol. 32, pp. 2311-2317, 1991.

[27] Hough, P. V. C., A method and means for recognizing complex patterns, US Patent 3069654, 1962.

[28] Deans, S. R., The Radon Transform and Some of Its Applications, Wiley, New York, 1983.

[29] Radon, J., Uber die bestimmung von funktionen durchihre integralwarte langs gewisser mannigfaltigkeiten, Bertichte Saechsiche Akad. Wissenschaften (Leipzig), Math. Phys. Klass, Vol. 69, pp. 262277, 1917.

[30] Kalviainen, H., Hirvonen, P., Xu, L., and Oja, E., Probabilistic and non-probabilistic Hough transforms: Overview and comparisons, Image Vision Comput., Vol. 13, pp. 239-252, 1995.

[31] Kassim, A., Tan, T., and Tan, K., A comparative study of efficient generalized Hough transforms techniques, Image Vision Comput., Vol. 17, pp. 737-748, 1999.

[32] Martelli, A., Edge detection using heuristic search methods, Comp. Graph. Image Proc., Vol. 1, pp. 169-182, 1972.

[33] Nilsson, N. J., Principles of Artificial Intelligence, Springer-Verlag, Berlin, 1982.

[34] Geiger, D., Gupta, A., Costa, A., and Vlontzos, J., Dynamic programming for detecting, tracking, and matching deformable contours, IEEE Trans. Patt. Anal. Mach. Intell., Vol. 17, pp. 294-302, 1995.

[35] Barret, W. A. and Mortensen, E. N., Interactive live-wire boundary detection, Med. Image Analy., Vol. 1, pp. 331-341, 1996.

[36] Zucker, S., Region growing: Childhood and adolescence, Comp. Graph. Image Proc., Vol. 5, pp. 382-399, 1976.

[37] Hebert, T. J., Moore, W. H., Dhekne, R. D., and Ford, P. V., Design of an automated algorithm for labeling the cardiac blood pool in gated SPECT images of radiolabeled red blood cells, IEEE Trans. Nucl. Sci.,

[38] Kim, J., Feng, D. D., Cai, T. W., and Eberl, S., Automatic 3D temporal kinetics segmentation of dynamic emission tomography image using adaptive region growing cluster analysis, In: Proceedings of 2002 IEEE Medical Imaging Conference, Vol. 3, IEEE, Norfolk, VA, pp. 1580-1583, 2002.

[39] Hartigan, J. A., Clustering Algorithms, Wiley, New York, 1975.

[40] Cooper, L., M-dimensional location models: Application to cluster analysis, J. Reg. Sci., Vol. 13, pp. 41-54, 1973.

[41] Bezdek, J. C., Ehrlich, R., and Full, W., FCM: The fuzzy c-means clustering algorithm, Comp. Geosci., Vol. 10, pp. 191-203, 1984.

[42] Ball, G. H. and Hall, D. J., A clustering technique for summarizing multi-variate data, Behav. Sci., Vol. 12, pp. 153-155, 1967.

[43] Anderberg, M. R., Cluster Analysis for Applications, Academic Press, New York, 1973.

[44] McLachlan, G. J. and Krishnan, T., The EM Algorithm and Extensions, Wiley, New York, 1997.

[45] Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active contour models, Int. J. Comput. Vis., Vol. 1, pp. 321-331, 1987.

[46] Terzopoulos, D. and Fleischer, K., Deformable models, Visual Comput., Vol. 4, pp. 306-331, 1988.

[47] Fischler, M. A. and Elschlager, R. A., The representation and matching of pictorial structures, IEEE Trans. Comput., Vol. 22, pp. 67-92, 1973.

[48] Widrow, B., The "rubber-mask" technique, Pattern Recogn., Vol. 5, pp. 175-211, 1973.

[49] McInerney, T. and Terzopoulos, D., Deformable models in medical image analysis: A survey, Med. Image Analy., Vol. 1, pp. 91-108, 1996.

[50] Mykkanen, J. M., Tohka, J., and Ruotsalainen, U., Automated delineation of brain structures with snakes in PET, In: Physiological Imaging of the Brain with PET, Gjedde, A., Hansen, S. B., Knudsen, G., and Paulson, O. B., eds., Academic Press, San Diego, pp. 39-43, 2001.

[51] Chiao, P. C., Rogers, W. L., Fessler, J. A., Clinthorne, N. H., and Hero, A. O., Motion-based estimation with boundary side information or boundary regularization, IEEE Trans. Med. Imaging, Vol. 13, pp. 227234, 1994.

[52] Chiao, P. C., Rogers, W. L., Clinthorne, N. H., Fessler, J. A., and Hero, A. O., Model-based estimation for dynamic cardiac studies using ECT, IEEE Trans. Med. Imaging, Vol. 13, pp. 217-226, 1994.

[53] Meltzer, C. C., Leal, J. P., Mayberg, H. S., Wagner, H. N., and Frost, J. J., Correction of PET data for partial volume effects in human cerebral cortex by MR imaging, J. Comput. Assist. Tomogr., Vol. 14, pp. 561-570, 1990.

[54] Muller-Gartner, H. W., Links, J. M., Price, J. L., Bryan, R. N., McVeigh, E., Leal, J. P., Davatzikos, C., and Frost, J. J., Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects, J. Cereb. Blood Flow Metab., Vol. 12, pp. 571-583, 1992.

[55] Fox, P. T., Perlmutter, J. S., and Raichle, M. E., A stereotatic method of anatomical localization for positron emission tomography, J. Comput. Assist. Tomogr., Vol. 9, pp. 141-153, 1985.

[56] Talairach, J., Tournoux, P., and Rayport, M., Co-planar Stereotaxic Atlas of the Human Brain, Thieme, Inc., New York, 1988.

[57] Thompson, P. and Toga, A., A surface-based technique for warping three-dimensional images of the brain, IEEE Trans. Med. Imaging, Vol. 15, pp. 402-417, 1996.

[58] Bremner, J. D., Bronen, R. A., De Erasquin, G., Vermetten, E., Staib, L. H., Ng, C. K., Soufer, R., Charney, D. S., and Innis, R. B., Development and reliability of a method for using magnetic resonance imaging for the definition of regions of interest for positron emission tomography, Clin. Pos. Imag., Vol. 1, pp. 145-159, 1998.

[59] Maintz, J. B. A. and Viergever, M. A., A survey of medical image registration, Med. Imag. Analy., Vol. 2, pp. 1-37, 1998.

[60] Pelizzari, C. A., Chen, G. T. Y., Spelbring, D. R., Weichselbaum, R. R., and Chen, C. T., Accurate three-dimensional registration of CT, PET and/or MR images of the brain, J. Comput. Assist. Tomogr., Vol. 13, pp. 20-26, 1989.

[61] Woods, R. P., Mazziotta, J. C., and Cherry, S. R., MRI-PET registration with automated algorithm, J. Comput. Assisted Tomogr., Vol. 17, pp. 536-546, 1993.

[62] Rogowska, J., Similarity methods for dynamic image analysis, In: Proceedings of International AMSE Conference on Signals and Systems, Vol. 2, Warsaw, Poland, 15-17 July 1991, pp. 113-124.

[63] Barber, D. C., The use of principal components in the quantitative analysis of gamma camera dynamic studies, Phys. Med. Biol., Vol. 25, pp. 283-292, 1980.

[64] Rogowska, J. and Wolf, G. L., Temporal correlation images derived from sequential MR scans, J. Comput. Assist. Tomogr., Vol. 16, pp. 784-788, 1992.

[65] Bandettini, P. A., Jesmanowicz, A., Wong, E. C., and Hyed, J. S., Processing strategies for time-course datasets in functional MRI of the human brain, Magn. Res. Med., Vol. 30, pp. 161-173, 1993.

[66] Rogowska, J., Preston, K., Hunter, G. J., Hamberg, L. M., Kwong, K. K., Salonen, O., and Wolf, G. L., Applications of similarity mapping in dynamic MRI, IEEE Trans. Med. Imaging, Vol. 14, pp. 480-486, 1995.

[67] Jolliffe, I., Principal Component Analysis, Springer, New York, 1986.

[68] Pearson, K., On lines and planes of closest fit to systems of points in space, Phil. Mag., Vol. 6, pp. 559-572, 1901.

[69] Hotelling, H., Analysis of a complex of statistical variables into principal components, J. Edu. Psycho., Vol. 24, pp. 417-441, 1933.

[70] Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P., Numerical Recipes in C. The Art of Scientific Computing, Cambridge University Press, New York, 1992.

[71] Golub, G. H. and Van Loan, C. F., Matrix Computations, 3rd edn., John Hopkins University Press, Baltimore, 1996.

[72] Moeller, J. R. and Strother, S. C., A regional covariance approach to the analysis of functional patterns in positron emission tomo-graphic data, J. Cereb. Blood Flow Metab., Vol. 11, pp. A121-A135, 1991.

[73] Friston, K. J., Frith, C. D., Liddle, P. F., and Frackowiak, R. S., Functional connectivity: The principal component analysis of large (PET) data sets, J. Cereb. Blood Flow Metab., Vol. 13, pp. 5-14, 1993.

[74] Pedersen, F., Bergstrom, M., and Langstrom, B., Principal component analysis of dynamic positron emission tomography images, Eur. J. Nucl. Med., Vol. 21, pp. 1285-1292, 1994.

[75] Strother, S. C., Anderson, J. R., Schaper, K. A., Sidtis, J. S., and Rottenberg, D. A., Linear models of orthogonal subspaces and networks from functional activation PET studies of the human brain, In: Information Processing in Medical Imaging, Bizais, Y., Barillot, C., and Di Paola, R., eds., Kluwer, Dordrecht, The Netherlands, pp. 299-310, 1995.

[76] Ardekani, B. A., Strother, S. C., Anderson, J. R., Law, I., Paulson, O. B., Kanno, I., and Rottenberg, D. A., On the detection of activation patterns using principal components analysis, In: Quantitative Functional Brain Imaging with Positron Emission Tomography, Carson, R. E., Daube-Witherspoon, M. E., and Herscovitch, P., eds., Academic Press, San Diego, pp. 253-257, 1998.

[77] Anzai, Y., Minoshima, S., Wolf, G. T., and Wahl, R. L., Head and neck cancer: Detection of recurrence with three-dimensional principal components analysis at dynamic FDG PET, Radiology, Vol. 212, pp. 285-290, 1999.

[78] Andersen, A. H., Gash, D. M., and Avison, M. J., Principal component analysis of the dynamic response measured by fMRI: A generalized linear systems framework, Mag. Res. Imag., Vol. 17, pp. 795-815,1999.

[79] Baumgartner, R., Ryner, L., Richter, W., Summers, R., Jarmasz, M., and Somorjai, R., Comparison of two exploratory data analysis methods for fMRI: Fuzzy clustering vs. principal component analysis, Mag. Res. Imag., Vol. 18, pp. 89-94, 2000.

[80] Correia, J., A bloody future for clinical PET? [editorial], J. Nucl. Med.,

[81] Iida, H., Rhodes, C. G., De Silva, R., Araujo, L. I., Bloomfield, P. M., Lammertsma, A. A., and Jones, T., Use of the left ventricular time-activity curve as a non-invasive input function in dynamic Oxygen-15-Water positron emission tomography, J. Nucl. Med., Vol. 33, pp. 1669-1677, 1992.

[82] Chen, K., Bandy, D., Reiman, E., Huang, S. C., Lawson, M., Feng, D., Yun, L. S., and Palant, A., Noninvasive quantification of the cerebral metabolic rate for glucose using positron emission tomography, 18F-fluoro-2-deoxyglucose, the Patlak method, and an image-derived input function, J. Cereb. Blood Flow Metab., Vol. 18, pp. 716-723,1998.

[83] Houston, A. S., The effect of apex-finding errors on factor images obtained from factor analysis and oblique transformation, Phys. Med. Biol., Vol. 29, pp. 1109-116, 1984.

[84] Nirjan, K. S. and Barber, D. C., Factor analysis of dynamic functionstud-ies using a priori physiological information, Phys. Med. Biol., Vol. 31, pp. 1107-1117, 1986.

[85] Samal, M., Karny, M., Surova, H., and Dienstbier, Z., Rotation to simple structure in factor analysis of dynamic radionuclide studies, Phys. Med. Biol., Vol. 32, pp. 371-382, 1987.

[86] Buvat, I., Benali, H., Frouin, F., Bazin, J. P., and Di Paola, R., Target apex-seeking in factor analysis on medical sequences, Phys. Med. Biol., Vol. 38, pp. 123-128, 1993.

[87] Sitek, A., Di Bella, E. V. R., and Gullberg, G. T., Factor analysis with a priori knowledge—Application in dynamic cardiac SPECT, Phys. Med. Biol., Vol. 45, pp. 2619-2638, 2000.

[88] Wu, H. M., Hoh, C. K., Buxton, D. B., Schelbert, H. R., Choi, Y., Hawkins, R. A., Phelps, M. E., and Huang, S. C., Factor analysis for extraction of blood time-activity curves in dynamic FDG-PET studies, J. Nucl. Med., Vol. 36, pp. 1714-1722, 1995.

[89] Wu, H. M., Huang, S. C., Allada, V., Wolfenden, P. J., Schelbert, H. R., Phelps, M. E., and Hoh, C. K., Derivation of input function from FDG-PET studies in small hearts, J. Nucl. Med., Vol. 37, pp. 1717-1722, 1996.

[90] Sitek, A., Di Bella, E. V. R., and Gullberg, G. T., Factor analysis of dynamic structures in dynamic SPECT imaging using maximum entropy, IEEE Trans. Nucl. Sci., Vol. 46, pp. 2227-2232, 1999.

[91] Sitek, A., Gullberg, G. T., andHuesman, R. H., Correction for ambiguous solutions in factor analysis using a penalized least squares objective, IEEE Trans. Med. Imaging, Vol. 21, pp. 2166-225, 2002.

[92] Ashburner, J., Haslam, J., Taylor, C., Cunningham, V. J., and Jones, T., A cluster analysis approach for the characterization of dynamic PET data, In: Quantification of Brain Function using PET, Myers, R., Cunningham, V., Bailey, D., and Jones, T., eds., Academic Press, San Diego, pp. 301-306, 1996.

[93] Acton, P. D., Pilowsky, L. S., Costa, D. C., and Ell, P. J., Multivariate cluster analysis of dynamic iodine-123 iodobenzamide SPET dopamine D2 receptor images in schizophrenia, Eur. J. Nucl. Med., Vol. 24, pp. 111118, 1997.

[94] Wong, K. P., Feng, D., Meikle, S. R., and Fulham, M. J., Segmentation of dynamic PET images using cluster analysis, IEEE Trans. Nucl. Sci., Vol. 49, pp. 200-207, 2002.

[95] Wong, K. P., Feng, D., Meikle, S. R., and Fulham, M. J., Simultaneous estimation of physiological parameters and the input function—In vivo PET data, IEEE Trans. Inform. Technol. Biomed., Vol. 5, pp. 67-76, 2001.

[96] Wong, K. P., Meikle, S. R., Feng, D., and Fulham, M. J., Estimation of input function and kinetic parameters using simulated annealing: Application in a flow model, IEEE Trans. Nucl. Sci., Vol. 49, pp. 707713, 2002.

[97] Cunningham, V. J. and Jones, T., Spectral analysis of dynamic PET studies, J. Cereb. Blood Flow Metab., Vol. 13, pp. 15-23, 1993.

[98] Zubal, I. G., Harrell, C. R., Smith, E. O., Rattner, Z., Gindi, G., and Hoffer, P. B., Computerized three-dimensional segmented human anatomy, Med. Phys., Vol. 21, pp. 299-302, 1994.

[99] Hoffman, E. J., Cutler, P. D., Digby, W. M., and Mazziotta, J. C., 3-D phantom to simulate cerebral blood flow and metabolic images for PET, IEEE Trans. Nucl. Sci., Vol. 37, pp. 616-620, 1990.

[100] Hawkins, R. A., Phelps, M. E., and Huang, S. C., Effects of temporal sampling, glucose metabolic rates, and disruptions of the blood-brain barrier on the FDG model with and without a vascular compartment: Studies in human brain tumors with PET, J. Cereb. Blood Flow Metab., Vol. 6, pp. 170-183, 1986.

[101] Akaike, H., A new look at the statistical model identification, IEEE Trans. Automatic Control, Vol. AC-19, pp. 716-723, 1974.

[102] Schwarz, G., Estimating the dimension of a model, Ann. Stat., Vol. 6, pp. 461-464, 1978.

[103] Hooper, P. K., Meikle, S. R., Eberl, S., and Fulham, M. J., Validation of post injection transmission measurements for attenuation correction in neurologic FDG PET studies, J. Nucl. Med., Vol. 37, pp. 128-136,

1996.

[104] Huang, S. C., Phelps, M. E., Hoffman, E. J., Sideris, K., Selin, C., and Kuhl, D. E., Noninvasive determination of local cerebral metabolic rate of glucose in man, Am. J. Physiol., Vol. 238, pp. E69-E82, 1980.

[105] Patlak, C. S., Blasberg, R. G., and Fenstermacher, J., Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data, J. Cereb. Blood Flow Metab., Vol. 3, pp. 1-7, 1983.

[106] Gunn, R. N., Lammertsma, A. A., and Cunningham, V. J., Parametric imaging of ligand-receptor interactions using a reference tissue model and cluster analysis, In: Quantitative Functional Brain Imaging with Positron Emission Tomography, Carson, R. E., Daube-Witherspoon, M. E., and Herscovitch, P., eds., Academic Press, San Diego, pp. 401406, 1998.

[107] Lammertsma, A. A. and Hume, S. P., Simplified reference tissue model for PET receptor studies, NeuroImage, Vol. 4, pp. 153-158, 1996.

[108] Gunn, R. N., Lammertsma, A. A., Hume, S. P., and Cunningham, V. J., Parametric imaging of ligand-receptor binding in PET using a simplified reference region model, NeuroImage, Vol. 6, pp. 279-287,

1997.

[109] Wong, K. P., Feng, D., Meikle, S. R., and Fulham, M. J., Non-invasive determination of the input function in PET by a Monte Carlo approach and cluster analysis, J. Nucl. Med., Vol. 42, No. 5(Suppl.), p. 183P, 2001.

[110] O'Sullivan, F., Imaging radiotracer model parameters in PET: A mixture analysis approach, IEEE Trans. Med. Imaging, Vol. 12, pp. 399-412, 1993.

[111] Kimura, Y., Hsu, H., Toyama, H., Senda, M., and Alpert, N. M., Improved signal-to-noise ratio in parametric images by cluster analysis, NeuroImage, Vol. 9, pp. 554-561, 1999.

[112] Bentourkia, M., A flexible image segmentation prior to parametric estimation, Comput. Med. Imaging Graphics, Vol. 25, pp. 501-506, 2001.

[113] Kimura, Y., Senda, M., and Alpert, N. M., Fast formation of statistically reliable FDG parametric images based on clustering and principal components, Phys. Med. Biol., Vol. 47, pp. 455-468, 2002.

[114] Zhou, Y., Huang, S. C., Bergsneider, M., and Wong, D. F., Improved parametric image generation using spatial-temporal analysis of dynamic PET studies, NeuroImage, Vol. 15, pp. 697-707, 2002.

[115] Bal, H., DiBella, E. V. R., and Gullberg, G. T., Parametric image formation using clustering for dynamic cardiac SPECT, IEEE Trans. Nucl. Sci., Vol. 50, pp. 1584-1589, 2003.

[116] Toyama, H., Takazawa, K., Nariai, T., Uemura, K., and Senda, M., Visualization of correlated hemodynamic and metabolic functions in cere-brovascular disease by a cluster analysis with PET study, In: Physiological Imaging of the Brain with PET, Gjedde, A., Hansen, S. B., Knudsen, G. M., and Paulson, O. B., eds., Academic Press, San Diego, pp. 301-304, 2001.

[117] Koh, W. J., Rasey, J. S., Evans, M. L., Grierson, J. R., Lewellen, T. K., Graham, M. M., Krohn, K. A., and Griffin, T. W., Imaging of hypoxia in human tumors with [F-18]fluoromisonidazole, Int. J. Radiat. Oncol. Biol. Phys., Vol. 22, pp. 199-212, 1992.

[118] Marsden, P. K., Personal communication, 2003.

[119] Huang, S. C., Hoffman, E. J., Phelps, M. E.,andKuhl, D. E., Quantitation in positron emission computed tomography, 2: Effects of inaccurate attenuation correction, J. Comput. Assist. Tomogr., Vol. 3, pp. 804-814, 1979.

[120] Dahlbom, M. and Hoffman, E. J., Problems in signal-to-noise ratio for attenuation correction in high-resolution PET, IEEE Trans. Nucl. Sci.,

[121] Huang, S. C., Carson, R. E., Phelps, M. E., Hoffman, E. J., Schelbert, H. R., and Kuhl, D. E., A boundary method for attenuation correction in positron computed tomography, J. Nucl. Med., Vol. 22, pp. 627-637, 1981.

[122] Xu, M., Luk, W. K., Cutler, P. D., and Digby, W. M., Local threshold for segmented attenuation correction of PET imaging of the thorax, IEEE Trans. Nucl. Sci., Vol. 41, pp. 1532-1537, 1994.

[123] Meikle, S. R., Dahlbom, M., and Cherry, S. R., Attenuation correction using count-limited transmission data in positron emission tomography, J. Nucl. Med., Vol. 34, pp. 143-144, 1993.

[124] Papenfuss, A. T., O'Keefe, G. J., and Scott, A. M., Segmented attenuation correction in whole body PET using neighbourhood EM clustering, In: 2000 IEEE Medical Imaging conference, IEEE Publication, Lyon, France, 2000.

[125] Bettinardi, V., Pagani, E., Gilardi, M. C., Landoni, C., Riddell, C., Rizzo, G., Castiglioni, I., Belluzzo, D., Lucignani, G., Schubert, S., and Fiazio, F., An automatic classification technique for attenuation correction in positron emission tomography, Eur. J. Nucl. Med., Vol. 26, pp. 447-458, 1999.

[126] Ogawa, S., Lee, T. M., Kay, A. R., and Tank, D. W., Brain magnetic resonance imaging with contrast dependent on blood oxygenation, Proc. Natl. Acad. Sci. USA, Vol. 87, pp. 9868-9872, 1990.

[127] Bullmore, E. and Brammer, B., Statistical methods of estimation and inference for functional MR image analysis, Magn. Reson. Med., Vol. 35, pp. 261-277, 1996.

[128] Lange, N., Statistical approaches to human brain mapping by functional magnetic resonance imaging, Stat. Med., Vol. 15, pp. 389-428, 1996.

[129] Moser, E., Diemling, M., and Baumgartner, R., Fuzzy clustering of gradient-echo functional MRI in the human visual cortex. Part II: Quantification, J. Magn. Reson. Imaging, Vol. 7, pp. 1102-1108, 1997.

[130] Goutte, C., Toft, P., Rostrup, E., Nielsen, F. A., and Hansen, L. K., On clustering fMRI time series, NeuroImage, Vol. 9, pp. 298-310, 1999.

[131] Fadili, M. J., Ruan, S., Bloyet, D., and Mazoyer, B., A multistep un-supervised fuzzy clustering analysis of fMRI time series, Hum. Brain Mapping, Vol. 10, pp. 160-178, 2000.

[132] Schmidt, K., Lucignani, G., Moresco, R. M., Rizzo, G., Gilardi, M. C., Messa, C., Colombo, F., Fazio, F., and Sokoloff, L., Errors introduced by tissue heterogeneity in estimation of local cerebral glucose utilization with current kinetic models of the [18F]fluorodeoxyglucose method, J. Cereb. Blood Flow Metab., Vol. 12, pp. 823-834, 1992.

[133] Popper, K. R., Normal science and its dangers, In: Criticism and the Growth of Knowledge, Lakatos, I. and Musgrave, A., eds., Cambridge University Press, Cambridge, pp. 51-58, 1970.

Your Metabolism - What You Need To Know

Your Metabolism - What You Need To Know

If you have heard about metabolism, chances are it is in relation to weight loss. Metabolism is bigger than weight loss, though, as you will learn later on. It is about a healthier, better you. If you want to fire up your metabolism and do not have any idea how to do it, you have come to the right place. If you have tried to speed up your metabolism before but do not see visible results, you have also come to the right place.

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