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Medical Image Segmentation: Methods and Applications in Functional Imaging

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Handbook of Biomedical Image Analysis

Abstract

Detection, localization, diagnosis, staging, and monitoring treatment responses are the most important aspects and crucial procedures in diagnostic medicine and clinical oncology. Early detection and localization of the diseases and accurate disease staging can improve the survival and change management in patients prior to planned surgery or therapy. Therefore, current medical practice has been directed toward early but efficient localization and staging of diseases, while ensuring that patients would receive the most effective treatment.

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Bibliography

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

    Google Scholar 

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

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  9. White, D. R., Houston, A. S., Sampson, W. F., and Wilkins, G. P., Intraand 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

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

    Google Scholar 

  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.

    Article  Google Scholar 

  14. Bezdek, J., Hall, L., and Clarke, L., Review of MR image segmentation techniques using pattern recognition, Med. Phys., Vol. 20, pp. 1033–1048, 1993.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  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.

    Article  Google Scholar 

  23. Chow, C. K. and Kaneko, T., Automatic boundary detection of the left ventricle from cineangiograms, Comput. Biomed. Res., Vol. 5, pp. 388–410, 1972.

    Article  Google Scholar 

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

    Google Scholar 

  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.

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  29. Radon, J., Über die bestimmung von funktionen durchihre integralwärte längs gewisser männigfaltigkeiten, Bertichte Säechsiche Akad. Wissenschaften (Leipzig), Math. Phys. Klass, Vol. 69, pp. 262–277, 1917.

    Google Scholar 

  30. Kalviainen, H., Hirvonen, P., Xu, L., and Oja, E., Probabilistic and nonprobabilistic Hough transforms: Overview and comparisons, Image Vision Comput., Vol. 13, pp. 239–252, 1995.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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

    MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

  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.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  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., Vol. 43, pp. 2299–2305, 1996.

    Article  Google Scholar 

  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.

    Google Scholar 

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

    MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  48. Widrow, B., The “rubber-mask” technique, Pattern Recogn., Vol. 5, pp. 175–211, 1973.

    Article  Google Scholar 

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

    Article  Google Scholar 

  50. Mykkänen, 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.

    Google Scholar 

  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. 227–234, 1994.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  54. Müller-Gärtner, 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.

    Google Scholar 

  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.

    Article  Google Scholar 

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

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  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.

    MATH  Google Scholar 

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

    MATH  Google Scholar 

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

    Google Scholar 

  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.

    Google Scholar 

  74. Pedersen, F., Bergström, M., and Långström, B., Principal component analysis of dynamic positron emission tomography images, Eur. J. Nucl. Med., Vol. 21, pp. 1285–1292, 1994.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  80. Correia, J., A bloody future for clinical PET? [editorial], J. Nucl. Med., Vol. 33, pp. 620–622, 1992.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  84. Nirjan, K. S. and Barber, D. C., Factor analysis of dynamic function studies using a priori physiological information, Phys. Med. Biol., Vol. 31, pp. 1107–1117, 1986.

    Article  Google Scholar 

  85. Šámal, M., Kárný, M., Surová, H., and Dienstbier, Z., Rotation to simple structure in factor analysis of dynamic radionuclide studies, Phys. Med. Biol., Vol. 32, pp. 371–382, 1987.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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 FDGPET studies in small hearts, J. Nucl. Med., Vol. 37, pp. 1717–1722, 1996.

    Google Scholar 

  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.

    Article  Google Scholar 

  91. Sitek, A., Gullberg, G.T., and Huesman, 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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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. 111–118, 1997.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 707–713, 2002.

    Article  Google Scholar 

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

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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. 401–406, 1998.

    Google Scholar 

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

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

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

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  116. Toyama, H., Takazawa, K., Nariai, T., Uemura, K., and Senda, M., Visualization of correlated hemodynamic and metabolic functions in cerebrovascular 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.

    Google Scholar 

  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.

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  120. Dahlbom, M. and Hoffman, E. J., Problems in signal-to-noise ratio for attenuation correction in high-resolution PET, IEEE Trans. Nucl. Sci., Vol. 34, pp. 288–293, 1987.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

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Wong, KP. (2005). Medical Image Segmentation: Methods and Applications in Functional Imaging. In: Suri, J.S., Wilson, D.L., Laxminarayan, S. (eds) Handbook of Biomedical Image Analysis. Topics in Biomedical Engineering International Book Series. Springer, Boston, MA. https://doi.org/10.1007/0-306-48606-7_3

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