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.).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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.
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.
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.
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.
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.
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.
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.
BrighamRAD Teaching Case Database Department of Radiology, Brigham and Women’s Hospital Harvard Medical School http://brighamrad.harvard.edu/education/online/tcd/tcd.html
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.
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.
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.
Choi H-K, Bengtsson E., “A Direct Way of Combining Texture and Color for Image Segmentation,” SCIA97, June 9–11, 1997.
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.
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.
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.
Dunn D., Higgins W. E., “Optimal Gabor Filters for Texture Segmentation,” IEEE Trans. on Image Processing, vol. 4, no. 7, July 1995.
Gesu V. D., Romeo L., “An Aplication of Integrated Clustering to MRI Segmentation,” Pattern Recognition Letters, vol. 15, pp. 731–738, 1994.
Ghanei Amir, “A Knowledge-Based Deformable Surface Model for Analysis of Medical Images,” PhD dissertation, The University of Michigan, 2001.
Grosky W., Mehrotra R.: “Image Database Management”. IEEE Computer, 22, 1989, 7–8.
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).
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.
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.
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.
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.
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) 115–122
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.
Pediatric Study Centers (PSC) for a MRI Study of Normal Brain Development — http://grants.nih.gov
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.
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.
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.
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.
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.
Tailarach J., Toumoux P., “Co-Planar Stereotactic Atlas of Human Brain”, New York, Georg Thieme (1988)
Tamura, H. Yokoya N.: “Image Database Systems: A Survey”. Pattern Recognition, Vol. 17, No. 1 (1984) 29–43.
Taxt T., Lundervold A., “Multispectral Analysis of the Brain Using Magnetic Resonance Imaging,” IEEE Trans, on Medical Imaging, vol. 13, no. 3, September 1994.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media New York
About this chapter
Cite this chapter
Stanchev, P.L., Fotouhi, F., Siadat, MR., Soltanian-Zadeh, H. (2003). Medical Multimedia Databases. In: Djeraba, C. (eds) Multimedia Mining. Multimedia Systems and Applications Series, vol 22. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1141-0_8
Download citation
DOI: https://doi.org/10.1007/978-1-4615-1141-0_8
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5412-3
Online ISBN: 978-1-4615-1141-0
eBook Packages: Springer Book Archive