Advertisement

Image Processing in Medicine

  • Baigalmaa Tsagaan
  • Hiromasa Nakatani
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 29)

Abstract

The development of medical imaging, such as x-ray computed tomographic (CT), magnetic resonance imaging (MRI) or ultrasound (US) imaging etc., has undergone revolutionary changes over the past three decades. Recently developed CT and MRI scanners are more powerful than previous machines providing the sharpest images with high resolution ever seen, without absorbing much radiation during procedures. Medical imaging is an important part of routine care nowadays[1]. It allows physicians to know what is going on inside a patient’s ever-complex body.

Keywords

Single Photon Emission Compute Tomography Compute Tomographic Colonography Deformable Model Virtual Endoscopy Positron Emission Tomogra 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Udupa, J.K., Herman, G.T.: 3D imaging in medicine. CRC Press (2000)Google Scholar
  2. 2.
    Dhawan, P.A.: Medical imaging analysis. Wiley-IEEE (2003)Google Scholar
  3. 3.
    Bankman, I.: Handbook of medical imaging: Processing and analysis. Academic Press (2000)Google Scholar
  4. 4.
    Napel, S.A.: Basic principles of spiral CT. In: Fishman, E.K., Jeffrey, R.B. (eds.) Principles and techniques of 3D spiral CT angiography, pp. 167–182. Raven Press (1995)Google Scholar
  5. 5.
    Lauterbur, P.C.: Image formation by induced local interactions: Examples of employing nuclear magnetic resonance. Nature 242, 190–191 (1973)CrossRefGoogle Scholar
  6. 6.
    Filler, A.G.: The history, development, and impact of computed imaging in neurological diagnosis and neurosurgery: CT, MRI, DTI. Int. J. Neurosurgery 7(1) (2010)Google Scholar
  7. 7.
    Deck, M.D., Henschke, C., Lee, B.C., DZimmerman, R., et al.: Computed tomography versus magnetic resonance imaging of the brain. A collaborative interinstitu-tional study. Clin. Imaging 13(1), 2–15 (1989)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Bailey, D.L., Townsend, D.W.: Positron emission tomography: basic sciences. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Wells, P.N.T.: Ultrasound imaging: review. Phys. Med. Biol. 51, R83–R98 (2006)CrossRefGoogle Scholar
  11. 11.
    Herman, G.T.: Fundamentals of computerized tomography: Image reconstruction from projection. Springer, Heidelberg (2009)Google Scholar
  12. 12.
    Rousset, O.G., Ma, Y., Evans, A.C.: Correction for partial volume effects in PET: Principle and validation. J. of Nuclear Medicine 39(5), 904–911 (1998)Google Scholar
  13. 13.
    Choplin, R.: Picture archiving and communication systems: an overview. Radiographics 12, 127–129 (1992)Google Scholar
  14. 14.
  15. 15.
    Khoo, V.S., Dearnaley, D.P., Finnigan, D.J., Padhani, A., et al.: Magnetic resonance imaging: Considerations and applications in radiotheraphy treatment planning. Radiother. Oncology 42, 1–15 (1997)CrossRefGoogle Scholar
  16. 16.
    Hajnal, J.V., Hill, D.L.G., Hawkes, D.J.: Medical image registration. CRC Press (2001)Google Scholar
  17. 17.
    Taylor, P.: Invited review: computer aids for decision-making in diagnostic radiology. Brit. J. Radiol. 68, 945–957 (1995)CrossRefGoogle Scholar
  18. 18.
    Ayache, N., Cinquin, P., Cohen, I., Cohen, L., et al.: Segmentation of complex three-dimensional medical objects: a challenge and a requirement for computer-assisted surgery planning and performance. In: Taylor, R.H., Lavallee, S., Burdea, G.C., Mosges, R. (eds.) Computer integrated surgery: technology and clinical applications, pp. 59–74. MIT Press (1996)Google Scholar
  19. 19.
    Yan, M.X.H., Karp, J.S.: An adaptive Bayesian approach to three-dimensional MR brain segmentation. In: XIVth Int. Conf. Infor. Proc. in Med. Imag., pp. 201–213 (1995)Google Scholar
  20. 20.
    Andreasen, N.C., Rajarethinam, R., Cizadlo, T., et al.: Automatic atlas-based vol-ume estimation of human brain regions from MR images. J. Comp. Assist. Tom. 20, 98–106 (1996)CrossRefGoogle Scholar
  21. 21.
    Osher, S., Fedkiw, P.R.: Level set methods and dynamic implicit surfaces. Springer, Heidelberg (2002)Google Scholar
  22. 22.
    Rajapakse, J.C., Giedd, J.N., Rapoport, J.L.: Statistical approach to segmentation of single-channel cerebral MR images. IEEE T. Med. Imag. 16, 176–186 (1997)CrossRefGoogle Scholar
  23. 23.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comp. Vision 1, 321–331 (1988)CrossRefGoogle Scholar
  24. 24.
    Davatzikos, C., Bryan, R.N.: Using a deformable surface model to obtain a shape representation of the cortex. IEEE T. Med. Imag. 15, 785–795 (1996)CrossRefGoogle Scholar
  25. 25.
    Staib, L.H., Duncan, J.S.: Boundary finding with parametrically deformable contour models. IEEE T. Pattern Anal. Mach. Intell. 14, 1061–1075 (1992)CrossRefGoogle Scholar
  26. 26.
    Tsagaan, B., Shimizu, A., Kobatake, H., Miyakawa, K.: Development of extraction method of kidneys from abdominal CT images using a three-dimensional de-formable model. Systems and Computers in Japan, 37–46 (2003)Google Scholar
  27. 27.
    Atkins, M.S., Mackiewich, B.T.: Fully automatic segmentation of the brain in MRI. IEEE T. Med. Imag. 17, 98–109 (1998)CrossRefGoogle Scholar
  28. 28.
    Kikinis, R., Shenton, M.E., Losifescu, D.V., McCarley, R.W., et al.: A Digital brain atlas for surgical planning, model-driven segmentation, and teaching. IEEE T. Vis. and Comp. Graph. 2(3), 232–241 (1996)CrossRefGoogle Scholar
  29. 29.
    Pham, D.L., Prince, J.L.: An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity in homogeneities. Patt. Rec. Let., 57–68 (1999)Google Scholar
  30. 30.
    Wismüller, A., Vietze, F., Dersch, D.R.: ’Segmentation with Neural Networks. In: Bankman, I.N., Frank, J., Brody, W., Zerhouni, E. (eds.) Handbook of medical imaging. Academic Press (2000)Google Scholar
  31. 31.
    Kay, J.: The EM algorithm in medical imaging. Stat. Methods Med. Res. 6(1), 55–75 (1997)CrossRefGoogle Scholar
  32. 32.
    Kapur, T., Grimson, E., Wells, W., Kikinis, R.: Segmentation of brain tissue from magnetic resonance images. Med. Im. Anal. 1, 109–127 (1996)CrossRefGoogle Scholar
  33. 33.
    Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Med. Im. Anal. 2, 1–36 (1998)CrossRefGoogle Scholar
  34. 34.
    Wells, W.M., et al.: Multi-modal volume registration by maximization of mutual Information. Med. Im. Anal. 1, 35–51 (1996)CrossRefGoogle Scholar
  35. 35.
    Fischl, B., et al.: High-resolution inter-subject averaging and a coordinate system for the cortical surface. Human Brain Mapping 8, 272–284 (1999)CrossRefGoogle Scholar
  36. 36.
    Risholm, P., Pieper, S., Samset, E., Wells, W.M.: Summarizing and Visualizing Uncertainty in Non-Rigid Registration. Med. Imag. Comp. Comp. Assist. Interv. 13(Pt 2), 554–561 (2010)Google Scholar
  37. 37.
    Thévenaz, P., Ruttimann, U.E., Unser, M.: A pyramid approach to subpixel registration based on intensity. IEEE T. Imag. Process. 7, 27–41 (1998)CrossRefGoogle Scholar
  38. 38.
    Studholme, C., Hill, D.L.G., Hawkes, D.J.: ’Automated 3D MR and PET brain image registration. Comp. Assist. Radiology, 248–253 (1995)Google Scholar
  39. 39.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE T. Pattern Anal. Mach. Intelli. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  40. 40.
    Davatzikos, C.: Nonlinear registration of brain images using deformable models. In: Mathematical methods in biomedical image analysis, pp. 94–103. IEEE Computer Society Press (1996)Google Scholar
  41. 41.
    Oguro, S., Tokuda, J., Elhawary, H., Haker, S., Kikinis, R., et al.: MRI signal intensity based B-spline nonrigid registration for pre- and intraoperative imaging during prostate brachytherapy. J. Magn. Reson. Imag. 30(5), 1052–1058 (2009)CrossRefGoogle Scholar
  42. 42.
    Grimson, W.E.L., et al.: An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization. IEEE T. Med. Imag. 15(2), 129–140 (1996)CrossRefGoogle Scholar
  43. 43.
  44. 44.
    Rusinek, H., Mourino, M.R., Firooznia, H., Weinreb, J.C., Chase, N.E.: Volumetric rendering of MR images. Radiology 171, 269–272 (1989)Google Scholar
  45. 45.
    Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3D surface construction algorithm. Computer Graphics (SIGGRAPH 1987 Proc.) 21, 163–169 (1987)CrossRefGoogle Scholar
  46. 46.
    Yoshida, H., Näppi, J., Nagata, K., Choi, J.R., Rockey, D.C.: Comparison of fully automated CAD with unaided human reading in CT colonography. In: Proc. Eight Int. Symp. Virtual Colonoscopy, pp. 96–97 (2007)Google Scholar
  47. 47.
    Petrick, N., Haider, M., Summers, R.M., Yeshwant, S.C., et al.: CT colonography with computer-aided detection as a second reader: observer performance study. Radiology 246(1), 148–56 (2008)Google Scholar
  48. 48.
    Murao, K., Ozawa, A., Yamanaka, T., et al.: Integrated CAD tools for CT lung cancer screening: automatic detection and real-time comparison with the past images on PACS. Radiology 221, 726 (2001)Google Scholar
  49. 49.
    Nakazawa, T., Goto, Y., Nakagawa, T., et al.: New CAD (computer-aided detection) system for lung cancer screening using CT image. Radiology 221, 727 (2001)Google Scholar
  50. 50.
  51. 51.
    Gilbert, F.J., Astley, S.M., Gillan, M.G.C., Agbaje, O.F., et al.: Single reading with computer-aided detection for screening mammography. The New England J. of Medicine 359, 1675–1684 (2008)CrossRefGoogle Scholar
  52. 52.
    Berlinger, N.: Robotic surgery-squeezing into tight places. New England J. of Medicine 354, 2099–2101 (2006)CrossRefGoogle Scholar
  53. 53.
    Haaker, R.G., Stockheim, M., Kamp, M., et al.: Computer-assisted navigation increases precision of component placement in total knee arthroplasty. Clin Orthop Relat. Res. 433, 152–9 (2005)Google Scholar
  54. 54.
    Marmulla, R., Niederdellmann, H.: Computer-assisted bone segment navigation. J. Cranio-Maxillofac. Surg. 26, 347–359 (1998)CrossRefGoogle Scholar
  55. 55.
    Geiger, B., Kikinis, R.: Simulation of endoscopy, AAAI Spring Symposium Series: Applications of Comp. Vis. Med. Imag. Proc., pp. 138–140 (1994)Google Scholar
  56. 56.
    Vining, V.C., Shifrin, R.Y., Grishaw, E.K., et al.: Virtual colonoscopy. Radiology 193, 446 (1994)Google Scholar
  57. 57.
    Lorensen, W.E., Jolesz, F.A., Kikinis, R.: The exploration of cross-sectional data with a virtual endoscope. In: Satava, R., Morgan, K. (eds.) Interactive Technology and the New Paradigm for Healthcare, pp. 221–230. IOS Press, Ohmsha (1995)Google Scholar
  58. 58.
    Robb, R.A., Hanson, D.P.: The ANALYZE software system for visualization and analysis in surgery simulation. In: Lavalle, S., Taylor, R., Burdea, G., Mosges, R. (eds.) Computer Integrated Surgery. MIT Press (1993)Google Scholar
  59. 59.
    Rubin, G.D., Beaulieu, C.F., Argiro, V., Ringl, H., et al.: Perspective volume render-ing of CT and MR images: Applications for endoscopic imaging. Radiology 199, 321–330 (1996)Google Scholar
  60. 60.
  61. 61.
    Rice, D.H., Schaefer, S.D.: Endoscopic paranasal sinus surgery, pp. 159–235. Raven Press (1993)Google Scholar
  62. 62.
    Tomoda, K., Murata, H., Ishimasa, H., Yamashita, J.: The evaluation of navigation surgery in nose and paranasal sinuses. Int. J. Comp. Assist. Radiology and Surgery 1, 311–312 (2006)CrossRefGoogle Scholar
  63. 63.
    Caversaccio, M., Bachler, R., Ladrach, K., Schroth, G., et al.: Frameless computer-aided surgery system for revision endoscopic sinus surgery. Otolaryngol. Head Neck. Surg. 122(6), 808–813 (2000)CrossRefGoogle Scholar
  64. 64.
    Grevers, G., Menauer, F., Leunig, A., Caversaccio, M., Kastenbauer, E.: Navigation surgery in diseases of the paranasal sinuses. Laryngorhinootologie 78(1), 41–46 (1999)CrossRefGoogle Scholar
  65. 65.
    Kherani, S., Javer, A.R., Woodham, J.D., Stevens, H.E.: Choosing a computer-assisted surgical system for sinus surgery. J. Otolaryngol. 32(3), 190–197 (2003)CrossRefGoogle Scholar
  66. 66.
    Kherani, S., Stammberver, H., Lackner, A., Reittner, P.: Image guided surgery of paranasal sinuses and anterior skull base-five years experience with the Insta-Trak-System. Rhinolgy 40, 1–9 (2002)Google Scholar
  67. 67.
    Yamashita, J., Yamauchi, Y., Mochimaru, M., Fukui, Y., Yokoyama, K.: Real-time 3-D model-based navigation system for endoscopic paranasal sinus surgery. IEEE T. Biomed. Eng. 46(1), 107–116 (1999)CrossRefGoogle Scholar
  68. 68.
    Tsagaan, B., Iwami, K., Abe, K., Nakatani, H., et al.: Development of navigation system for paranasal sinus surgery. In: Int. Symp. Comp. Methods on Biomechanics and Biomedical Engineering, vol. 1, pp. 1–8 (2006)Google Scholar
  69. 69.
    Tsagaan, B., Abe, K., Iwami, K., Nakatani, H., et al.: Newly developed navigation system for paranasal sinus surgery. J. Comp. Assist. Radiology and Surgery 1(1), 502–503 (2006)Google Scholar
  70. 70.
    Horn, B.: Robot Vision. ch.8. MIT Press (1986)Google Scholar
  71. 71.
    Ohta, N., Kanatani, K.: Optimal estimation of three-dimensional rotation and reliability evaluation. In: Proc. Computer Vision, vol. 1, pp. 175–187 (1998)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Baigalmaa Tsagaan
    • 1
  • Hiromasa Nakatani
    • 1
  1. 1.Shizuoka UniversityShizuokaJapan

Personalised recommendations