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Reconstruction, Visualization and Analysis of Medical Images

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Handbook of Data Visualization

Part of the book series: Springer Handbooks Comp.Statistics ((SHCS))

Abstract

Advances in medical imaging systems havemade significant contributions to medical diagnoses and treatments by providing anatomic and functional information about human bodies that is difficult to obtain without these techniques. These modalities also generate large quantities of noisy data that need modern techniques of computational statistics for image reconstruction, visualization and analysis. This article will report recent research in this area and suggest challenges that will need to be addressed by future studies. Specifically, I will discuss computational statistics for positron emission tomography, ultrasound images and magnetic resonance images from the perspectives of image reconstruction, image segmentation and vision model-based image analysis.

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References

  • Burckhardt, C.B. (1978). Speckle in ultrasound B-mode scans. IEEE Trans Ultrasonics, 25:1–6.

    Google Scholar 

  • Censor, Y. (1983). Finite Series-Expansion Reconstruction Methods. Proc IEEE, 71:409–419.

    Google Scholar 

  • Chatziioannou, A., Qi, J., Moore, A., Annala, A., Nguyen, K., Leahy, R. and Cherry, S. (2000). Comparison of 3D MAP and filtered backprojection algorithms for high resolution animal imaging with microPET. IEEE Trans Med Imaging, 19:507–512.

    Article  Google Scholar 

  • Chen, C.H. and Li, K.C. (1998). Can SIR be as popular as multiple linear regression? Stat Sinica, 8:289–316.

    MATH  Google Scholar 

  • Chen, C.H. and Li, K.C. (2001). Generalization of Fisher’s linear discriminant analysis via the approach of sliced inverse regression. J Korean Stat Soc, 30:193–217.

    Google Scholar 

  • Chen, C.M., Lu, H.H.S. and Lin, Y.C. (2000). An Early Vision Based Snake Model for Ultrasound Image Segmentation. Ultrasound Med Biol, 26:273–285.

    Article  MATH  Google Scholar 

  • Chen, C.M. and Lu, H.H.S. (2001). An Adaptive Snake Model for Ultrasound Image Segmentation: Modified Trimmed Mean Filter, Ramp Integration and Adaptive Weighting Parameters. Ultrason Imag, 22:214–236.

    MathSciNet  Google Scholar 

  • Chen, C.M., Lu, H.H.S. and Han, K.C. (2001). A Textural Approach Based on Gabor Functions for Texture Edge Detection in Ultrasound Images. Ultrasound Med Biol, 27:513–534.

    Google Scholar 

  • Chen, C.M., Lu, H.H.S. and Hsiao, A.T. (2001). A Dual Snake Model of High Penetrability for Ultrasound Image Boundary Extraction. Ultrasound Med Biol, 27:1651–1665.

    Article  Google Scholar 

  • Chen, C.M., Lu, H.H.S. and Huang, Y.S. (2002). Cell-Based Dual Snake Model: A New Approach to Extracting Highly Winding Boundaries in The Ultrasound Images. Ultrasound Med Biol, 28:1061–1073.

    Article  Google Scholar 

  • Chen, C.M., Lu, H.H.S. and Chen, Y.L. (2003). A Discrete Region Competition Approach Incorporating Weak Edge Enhancement for Ultrasound Image Segmentation. Pattern Recogn Lett, 24:693–704.

    Article  Google Scholar 

  • Chen, T.B., Chen, J.C., Lu, H.H.S. and Liu, R.S. (2007). MicroPET Reconstruction with Random Coincidence Correction via a Joint Poisson Model. Medical Engineering & Physics, (in press).

    Google Scholar 

  • Dunn, D., Higgins, W.E. and Wakeley, J. (1994). Texture segmentation using 2-D Gabor elementary functions. IEEE Trans Pattern Anal Mach Intell, 16:130–149.

    Article  Google Scholar 

  • Fessler, J.A. (1994). Penalized Weighted Least-Squares Image Reconstruction for Positron Emission Tomography. IEEE Trans Med Imag, 13:292–300.

    Google Scholar 

  • Fessler, J.A. and Hero, A.O. (1994). Space-alternating generalized expectation-maximization algorithm. IEEE Trans Signal Proc, 42:2664–2677.

    Article  Google Scholar 

  • Fessler, J.A. and Hero, A.O. (1995). Penalized maximum-likelihood image reconstruction using space-alternating generalized expectation-maximization algorithms. IEEE Trans Imag Process, 4:1417–1429.

    Article  Google Scholar 

  • Goodman, J.W. (1985). Statistical Optics. Wiley, New York.

    Google Scholar 

  • Herman, G.T. (1980). Image Reconstruction From Projections: The Fundamentals of Computerized Tomography. Academic, New York.

    MATH  Google Scholar 

  • Herman, G.T., Lent, A. and Hurwitz, H. (1980). A Storage-Efficient Algorithm for Finding the Regularized Solution of a Large Inconsistent system of Equations. J Inst Math Applic, 25:361–366.

    Article  MATH  MathSciNet  Google Scholar 

  • Jain, A.K. and Farrokhnia, F. (1991). Unsupervised texture segmentation using Gabor filters. Pattern Recogn, 24:1167–1186.

    Article  Google Scholar 

  • Kevles, B.H. (1997). Naked to the Bone: Medical Imaging in the Twentieth Century. Rutgers University Press, Piscataway, NJ.

    Google Scholar 

  • Li, K.C. (1991). Sliced inverse regression for dimensional reduction (with discussion). J Am Stat Assoc, 86:316–342.

    Article  MATH  Google Scholar 

  • Li, K.C. (2000). High dimensional data analysis via the SIR/PHD approach. Lecture Notes, Department of Statistics, UCLA, Los Angeles, CA (http://www.stat.ucla.edu/∼ kcli/sir-PHD.pdf).

    Google Scholar 

  • Lu, H.H.S., Chen, C.M. and Yang, I.H. (1998). Cross-Reference Weighted Least Square Estimates for Positron Emission Tomography. IEEE Trans Med Imag, 17:1–8.

    Article  Google Scholar 

  • Lu, H.H.S. and Tseng, W.J. (1997). On Accelerated Cross-Reference Maximum Likelihood Estimates for Positron Emission Tomography. Proc IEEE Nucl Sci Symp, 2:1484–1488.

    Google Scholar 

  • Malik, J. and Perona, P. (1990). Preattentive texture discrimination with early vision mechanisms. J Opt Soc Am A, 7:923–932.

    Article  Google Scholar 

  • Meng, X.L. and Rubin, D.B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika, 80:267–278.

    Article  MATH  MathSciNet  Google Scholar 

  • Ouyang, X., Wong, W.H., Johnson, V.E., Hu, X. and Chen, C.T. (1994). Incorporation of Correlated Structural Images in PET Image Reconstruction. IEEE Trans Med Imag, 13:627–640.

    Article  Google Scholar 

  • Politte, F.G. and Snyder, D.L. (1991). Corrections for Accidental Coincidences and Attenuation in Maximum-Likelihood Image Reconstruction for Positron-Emission Tomography. IEEE Trans Med Imag, 10:82–89.

    Article  Google Scholar 

  • Prince, J.L. and Links, J. (2005). Medical Imaging Signals and Systems. Prentice Hall, Upper Saddle River, NJ.

    Google Scholar 

  • Shepp, L.A. and Vardi, Y. (1982). Maximum Likelihood Reconstruction for Emission Tomography. IEEE Trans Med Imag, 1:113–122.

    Google Scholar 

  • Soatto, S., Doretto, G. and Wu, Y. (2001). Dynamic textures. Intl Conf Comput Vis, 439–446.

    Google Scholar 

  • Spinks, T.J., Jones, T., Gilardi, M.C. and Heather, J.D. (1988). Physical Performance of the Latest Generation of Commercial Positron Scanner. IEEE Trans Nucl Sci, 35:721–725.

    Article  Google Scholar 

  • Suetens, P. (2002). Fundamentals of Medical Imaging. Cambridge University Press, Cambridge.

    Google Scholar 

  • Tan, T.N. (1995). Texture edge detection by modelling visual cortical channels. Pattern Recogn, 28:1283–1298.

    Article  Google Scholar 

  • Tu, K.Y., Chen, T.B., Lu, H.H.S., Liu, R.S., Chen, K.L., Chen, C.M. and Chen, J.C. (2001). Empirical Studies of Cross-Reference Maximum Likelihood Estimate Reconstruction for Positron Emission Tomography. Biomed Eng – Appl Basis Commun, 13:1–7.

    Google Scholar 

  • Vardi, Y., Shepp, L.A. and Kaufman, L. (1985). A Statistical Model for Positron Emission Tomography. J Am Stat Assoc, 80:8–20.

    Article  MATH  MathSciNet  Google Scholar 

  • Weaver, H.J. (1983). Applications of Discrete and Continuous Fourier Analysis. Wiley, New York.

    MATH  Google Scholar 

  • Wu, H.M. and Lu, H.H.S. (2004). Supervised motion segementation by spatial-frequential analysis and dynamic sliced inverse regression. Stat Sinica, 14:413–430.

    MATH  MathSciNet  Google Scholar 

  • Wu, Y., Zhu, S.C. and Guo, C. (2002). Statistical modelling of texture sketch. Proc Eur Conf Comp Vis, 240–254.

    Google Scholar 

  • Wu, Y., Zhu, S.C. and Liu, X. (2000). Equivalence of Julesz texture ensembles and FRAME models. Int J Comp Vis, 38:247–265.

    Article  MATH  Google Scholar 

  • Xiang, D. and Wahba, G. (1996). A generalized approximate cross validation for smoothing splines with non-Gaussian data. Stat Sinica, 6:675–692.

    MATH  MathSciNet  Google Scholar 

  • Zhu, S.C., Liu, X. and Wu, Y. (2000). Exploring texture ensembles by efficient Markov Chain Monte Carlo – towards a ‘Trichromacy’ theory of texture. IEEE Trans Pattern Anal Mach Intell, 22:554–569.

    Article  Google Scholar 

  • Zhu, S.C., Wu, Y. and Mumford, D.B. (1998). Filter, random field, and maximum entropy (FRAME): towards a unified theory for texture modelling. Int J Comp Vis, 27:107–126.

    Article  Google Scholar 

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Lu, HS. (2008). Reconstruction, Visualization and Analysis of Medical Images. In: Handbook of Data Visualization. Springer Handbooks Comp.Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-33037-0_31

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