Advertisement

Medical Multimedia Databases

  • Peter L. Stanchev
  • Farshad Fotouhi
  • Mohammad-Reza Siadat
  • Hamid Soltanian-Zadeh
Chapter
Part of the Multimedia Systems and Applications Series book series (MMSA, volume 22)

Abstract

Recent advances in computer technology and software have resulted in a shift from paper-based medical record to electronic medical record systems. Although electronic databases have been used in medical research for analysis of data for years, computerized information systems rarely have been used for collection of data during actual patient-physician interactions. The essential parts on this data are the medical images. Management of medical images has become a major issue for the development of healthcare in the last decades. Several medical devices produce medical images, such as: X-ray, X-ray computed tomography (CT), magnetic resonance (MR), magnetic resonance spectroscopy (MRS), single photon emission computer tomography (SPECT), positron emission tomography (PET), ultrasound, electrical source (ESI), electrical impedance tomography (EIT), magnetic source (MS) and magnetic optical images. Medical systems suppose to have tools to analyze multidimensional and multimodal medical images in order to improve diagnosis and therapy, especially when therapy is guided by medical images (video-surgery, interventional radiology, radiotherapy, etc.).

Keywords

Image Segmentation Image Retrieval Electrical Impedance Tomography Single Photon Emission Computer Tomography Query Module 
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]
    Alfano B., Brunetti A., Covelli E. M., Quarantelli, M., Panico, M. R., Ciarmiello, A., Salvatore, M., “Unsupervised, Automated Segmentation of the Normal Brain Using a Multispectral Relaxometric Magnetic Resonance Approach,” MRM, vol. 37, pp. 84–93, 1997.Google Scholar
  2. [2]
    Ashton, M., Berg, J., Parker, K. J., Weisberg, J., Chen, C. W., Ketonen, L., “Segmentation and Feature Extraction Techniques, with Applications to MRI Head Studies,” MRM, vol. 33, pp. 670–677, 1995.Google Scholar
  3. [3]
    Atkins M. S., Mackiewich B. T., “Fully Automatic Segmentation of the Brain in MRI,” IEEE Trans. on Medical Imaging, vol. 17, no. 1, February 1998.Google Scholar
  4. [4]
    Barra V., Boire J-Y., “Tissue Segmentation on MR Images of the Brain by Possibilistic Clustering on a 3D Wavelet Representation,” J. of Magnetic Resonance Imaging, vol. 11, pp. 267–278, 2000.CrossRefGoogle Scholar
  5. [5]
    Bengtsson E., Nordin B., Pederson F., “MUSE — A New Tool for Interactive Image Analysis and Segmentation based on Multivariate Statistics,” Computer Methods and Programs in Biomedicine, Elsevier Publishers, vol. 42, pp. 181–200, 1994.CrossRefGoogle Scholar
  6. [6]
    Bensaid A. M., Hall L. O., Bezdek J. C., et. al., “Validity-Guided (Re)Clustering with Applications to Image Segmentation,” IEEE Trans, on Fuzzy Systems, vol. 4, no. 2, May 96.Google Scholar
  7. [7]
    Black S.E., Moffat S.D., Yu D.C., Parker J., Stanchev P., Bronskill M. “Callosal atrophy correlates with temporal lobe volume and mental status in Alzheimer’s disease.” Canadian Journal of Neurological Sciences. 27(3):204–9, 2000 Aug.Google Scholar
  8. [8]
    BrighamRAD Teaching Case Database Department of Radiology, Brigham and Women’s Hospital Harvard Medical School http://brighamrad.harvard.edu/education/online/tcd/tcd.html
  9. [9]
    Brown M. S., McNitt-Gray M. F., Mankovich N. J., Goldin J. G., Hiller J., Wilson L. S., Aberle D. R., “Method for Segmentation Chest CT Image Data Using an Anatomical Model: Preliminary Results,” IEEE Trans. on Medical Imaging, vol. 16, no. 6, December 1997.Google Scholar
  10. [10]
    Chang M. M., Tekalp A. M., Sezan M. I., “Bayesian Segmentation of MR Images Using 3-D Gibbsian Priors,” SPIE vol. 1903, Image and Video Processing, 1993.Google Scholar
  11. [11]
    Chen J-L., Kundu A., “Unsupervised Texture Segmentation Using Multichannel Decomposition and Hidden Markov Models,” IEEE Trans, on Image Processing, vol. 4, no. 5, May, 1995.Google Scholar
  12. [12]
    Choi H-K, Bengtsson E., “A Direct Way of Combining Texture and Color for Image Segmentation,” SCIA97, June 9–11, 1997.Google Scholar
  13. [13]
    Clarke L. P., Velthuizen R. P., Camacho M. A., Heine, et al., “MRI Segmentation: Methods and Applications, Elsevier Publishers,” Magnetic Resonance Imaging, vol. 13, no. 3, 1995.Google Scholar
  14. [14]
    Cline H., Dumoulin C, Hart H., Lorensen W., Ludke S., “3D Reconstruction of Brain from Magnetic Resonance Images Using a Connectivity Algorithm”, Magnetic Resonance Imaging, Vol. 5 (1987) 445–352.CrossRefGoogle Scholar
  15. [15]
    Cocosco CA., Kollokian V., Kwan R.K.-S., Evans A.C.: “BrainWeb: Online Interface to a 3D MRI Simulated Brain Database”, Neurolmage, vol.5, no.4, part 2/4, S425, 1997 - Proceedings of 3-rd International Conference on Functional Mapping of the Human Brain, Copenhagen, May 1997.Google Scholar
  16. [16]
    Dunn D., Higgins W. E., “Optimal Gabor Filters for Texture Segmentation,” IEEE Trans. on Image Processing, vol. 4, no. 7, July 1995.Google Scholar
  17. [17]
    Gesu V. D., Romeo L., “An Aplication of Integrated Clustering to MRI Segmentation,” Pattern Recognition Letters, vol. 15, pp. 731–738, 1994.CrossRefGoogle Scholar
  18. [18]
    Ghanei Amir, “A Knowledge-Based Deformable Surface Model for Analysis of Medical Images,” PhD dissertation, The University of Michigan, 2001.Google Scholar
  19. [19]
    Grosky W., Mehrotra R.: “Image Database Management”. IEEE Computer, 22, 1989, 7–8.Google Scholar
  20. [20]
    Grosky W., Stanchev P., “Object-Oriented Image Database Model”, 16th International Conference on Computers and Their Applications (CATA-2001), March 28–30, 2001, Seattle, Washington (94–97).Google Scholar
  21. [21]
    Gudivada V., Raghavan V., Vanapipat K.,: “A United Approach to Data Modeling and Retrieval for a Class of Image Database Applications”, in Subrahmanian V., Jajodia S., Multimedia Database Systems, Springer 1996, 37–78.Google Scholar
  22. [22]
    Hall L. O., Bensaid A. M., Clarke L. P., Velthuizen R. P., Silbiger M. S., Bezdek J. C., “A Comparison of Neural Network and Fuzzy Clustering Techniques in Segmenting Magnetic Resonance Images of the Brain,” IEEE Trans. on Neural Networks, vol. 3, no. 5, September 1992.Google Scholar
  23. [23]
    Hofmann T., Puzicha J., Buhmann J. M., “Unsupervised Texture Segmentation in a Deterministic Annealing Framework,” IEEE Pattern Analysis and Machine Intelligence, vol. 20, no. 8, August 1998.Google Scholar
  24. [24]
    Kidron D. Black SE. Stanchev P. Buck B. Szalai JP. Parker J. Szekely C. Bronskill MJ., “Quantitative MR volumetry in Alzheimer’s disease. Topographic markers and the effects of sex and education”, Neurology. 49(6): 1504–12, 1997 Dec.CrossRefGoogle Scholar
  25. [25]
    Kohn M., Tanna N., Hermas G., Resnick S., Mozley D., Gur., Alavi A., Zimmerman R., Gur R., “Analysis of Brain and Cerebrospinal Fluid Volumes with MR Imaging”, Radiology 178 (1991) 115–122Google Scholar
  26. [26]
    Lundervold A., Storvik G., “Segmentation of Brain Parenchyma and Cerebrospinal Fluid in Multispectral Magnetic Resonance Images,” IEEE Trans. on Medical Imaging, vol. 14, no. 2, June 1995.Google Scholar
  27. [27]
    Pediatric Study Centers (PSC) for a MRI Study of Normal Brain Development — http://grants.nih.gov
  28. [28]
    Siadat M., Soltanian-Zadeh H., “An Intelligent Approach for Locating Hippocampus in Brain MRI,” Proceedings of the 16 th IASTED International Conference, Garmisch-Partenkirchen, Germany, Feb. 23–25, 1998.Google Scholar
  29. [29]
    Smith K. R., Kendrick L. A., “Bayesian Computer Vision Methods for Improved Tumor Localization and Delineation,” IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2–9, 1991.Google Scholar
  30. [30]
    Soltanian-Zadeh H., Nezafat R., and Windham J.P.: “Is There Texture Information in Standard Brain MRI?” Proceedings of SPIE Medical Imaging 1999: Image Processing Conference, San Diego, CA, Feb. 1999.Google Scholar
  31. [31]
    Soltanian-Zadeh H., Siadat M. R., “Knowledge-Based Localization of Hippocampus in Human Brain MRI,” Proceedings of SPIE Medical Imaging 1999, San Diego, CA, Feb. 20–26, 1999. SPIE MI99 poster honorable mention award winner.Google Scholar
  32. [32]
    Stanchev, P., ‘General Image Database Model,’ Visual Information and Information Systems, Proceedings of the Third Conference on Visual Information Systems, Huijsmans, D. Smeulders A., (Eds.) Lecture Notes in Computer Science, Volume 1614 (1999), pp. 29–36.Google Scholar
  33. [33]
    Tailarach J., Toumoux P., “Co-Planar Stereotactic Atlas of Human Brain”, New York, Georg Thieme (1988)Google Scholar
  34. [34]
    Tamura, H. Yokoya N.: “Image Database Systems: A Survey”. Pattern Recognition, Vol. 17, No. 1 (1984) 29–43.CrossRefGoogle Scholar
  35. [35]
    Taxt T., Lundervold A., “Multispectral Analysis of the Brain Using Magnetic Resonance Imaging,” IEEE Trans, on Medical Imaging, vol. 13, no. 3, September 1994.Google Scholar
  36. [36]
    Teuner A., Pichler O., Hosticka B. J., “Unsupervised Texture Segmentation of Images Using Tuned Matched Gabor Filters,” IEEE Trans. On Image Processing, vol. 4, no. 6, June 1995.Google Scholar
  37. [37]
    Vaidyanathan M., Clarke L. P., Velthuizen R. P., Phuphanich S., et al., “Comparison of Supervised MRI Segmentation Methods for Tumor Volume Determination During Therapy,” Magnetic Resonance Imaging, vol. 13, no. 5, pp. 719–728, 1995.CrossRefGoogle Scholar
  38. [38]
    Vinitski S., Gonzalez C., Burnett C., et al., “Tissue Segmentation in MRI as an Informative Indicator of Disease Activity in the Brain,” Image Analysis and Processing: 8 th International Conference, Italy, September 3–15,1995.Google Scholar
  39. [39]
    Wells W. M., Grimson W. E. L., Kikinis R., Jolesz F. A., “Adaptive Segmentation of MRI Data,” IEEE Trans, on Medical Imaging, vol. 15, no. 4, August 1996.Google Scholar
  40. [40]
    Yan M. X. H., Karp J. S., “Segmentation of 3D Brain MR Using an Adaptive K-means Clustering Algorithm,” Proceedings of the 1994 Nuclear Science Symposium and Medical Imaging Conference. Part 4 (of 4), Norfolk, VA, USA, pp. 1529–1533, 1995.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Peter L. Stanchev
    • 1
  • Farshad Fotouhi
    • 1
  • Mohammad-Reza Siadat
    • 2
  • Hamid Soltanian-Zadeh
    • 1
  1. 1.Kettering UniversityUSA
  2. 2.Wayne State UniversityUSA

Personalised recommendations