Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor’s opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems. 1. Introduction Access to medical care is sometimes very difficult to be reached from people living in rural and underserved areas. The implementation of systems support different telehealth technologies as well as on evidence-based medicine. A fundamental change in the management and delivery of healthcare, by leveraging telehealth applications and solutions, is crucial to get the most cost-effective and efficient use out of available healthcare resources. It is therefore crucial to develop a general information exchanged e-medical system to enable its users to perform online and offline medical consultations. This means by consultation of additional medical experts, medical diagnosis can be stated more precisely, and certified. During the medical diagnosis, image analysis techniques combined with doctor’s opinions could be useful for final medical decisions. Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where
References
[1]
M. Hebert, “Telehealth success: evaluation framework development,” Medinfo, vol. 10, no. 2, pp. 1145–1149, 2001.
[2]
W. De Neve, C. De Wagter, K. De Jaeger et al., “Planning and delivering high doses to targets surrounding the spinal cord at the lower neck and upper mediastinal levels: static beam-segmentation technique executed with a multileaf collimator,” Radiotherapy and Oncology, vol. 40, no. 3, pp. 271–279, 1996.
[3]
U. Isacsson, H. Hagberg, K.-A. Johansson, A. Montelius, B. Jung, and B. Glimelius, “Potential advantages of protons over conventional radiation beams for paraspinal tumours,” Radiotherapy and Oncology, vol. 45, no. 1, pp. 63–70, 1997.
[4]
A. Pirzkall, F. Lohr, B. Rhein et al., “Conformal radiotherapy of challenging paraspinal tumors using a multiple arc segment technique,” International Journal of Radiation Oncology Biology Physics, vol. 48, no. 4, pp. 1197–1204, 2000.
[5]
A. W. Powell III, “Reengineering health care delivery using telemedicine,” in Proceedings of the Medical Technology Symposium, pp. 371–374, Honolulu, Hawaii, USA, 1998.
[6]
G. Kontaxakis, S. Walter, and G. Sakas, “Eu-teleInViVo: an integrated portable telemedicine workstation featuring acquisition, processing and transmission over low-bandwidth lines of 3D ultrasound volume images,” in Proceedings of the Information Technology Applications in Biomedicine, IEEE EMBS International Conference (IEEE-EMBS '00), pp. 158–163, Arlington, Va, USA, November 2000.
[7]
T@LEMED Project web site: http://www.alis-telemed.net/.
[8]
G. Sherouse, C. Mosher, K. Novins, J. Rosemann, and E. L. Chaney, “Virtual simulation: concept and implementation,” in Proceedings of the 9th International Conference of the Use of Computers in Radiation Therapy (ICCR '87), pp. 433–436, North Holland Publishing, Scheveningen, The Netherlands, 1987.
[9]
Y. Nagata, T. Nishidai, M. Abe et al., “CT simulator: a new 3-D planning and simulating system for radiotherapy: part 2. Clinical application,” International Journal of Radiation Oncology Biology Physics, vol. 18, no. 3, pp. 505–513, 1990.
[10]
T. Nishidai, Y. Nagata, M. Takahashi et al., “CT simulator: a new 3-D planning and simulating system for radiotherapy: part 1. Description of system,” International Journal of Radiation Oncology Biology Physics, vol. 18, no. 3, pp. 499–504, 1990.
[11]
J. Rosenman, S. L. Sailer, G. W. Sherouse, E. L. Chaney, and J. E. Tepper, “Virtual simulation: initial clinical results,” International Journal of Radiation Oncology Biology Physics, vol. 20, no. 4, pp. 843–851, 1991.
[12]
G. W. Sherouse and E. L. Chaney, “The portable virtual simulator,” International Journal of Radiation Oncology Biology Physics, vol. 21, no. 2, pp. 475–482, 1991.
[13]
C. A. Perez, J. A. Purdy, W. Harms et al., “Design of a fully integrated three-dimensional computed tomography simulator and preliminary clinical evaluation,” International Journal of Radiation Oncology Biology Physics, vol. 30, no. 4, pp. 887–897, 1994.
[14]
E. K. Butker, D. J. Helton, J. W. Keller, L. L. Hughes, T. Crenshaw, and L. W. Davis, “A totally integrated simulation technique for three-field breast treatment using a CT simulator,” Medical Physics, vol. 23, no. 10, pp. 1809–1814, 1996.
[15]
G. T. Y. Chen, C. A. Pelizzari, and S. Vijayakumar, “Imaging: the basis for effective therapy,” Frontiers of Radiation Therapy and Oncology, vol. 29, pp. 31–42, 1996.
[16]
J. M. Michalski, J. A. Purdy, W. Harms, and J. W. Matthews, “The CT-simulation 3-D treatment planning process,” Frontiers of Radiation Therapy and Oncology, vol. 29, pp. 43–56, 1996.
[17]
G. Sakas, “Interactive volume rendering of large fields,” The Visual Computer, vol. 9, no. 8, pp. 425–438, 1993.
[18]
W. Cai, “Transfer functions in DRR volume rendering,” in Proceedings of the 13th International Congress and Exhibition, Computer Assisted Radiology and Surgery (CARS '99), pp. 23–26, Paris, France, June 1999.
[19]
G. Karangelis, 3D simulation of external beam radiotherapy, Ph.D. thesis, University of Darmstadt, Darmstadt, Germany, 2004.
[20]
M. Levoy, “Display of surfaces from volume data,” IEEE Computer Graphics and Applications, vol. 8, no. 3, pp. 29–37, 1988.
[21]
N. Max, “Optical models for direct volume rendering,” IEEE Transactions on Visualization and Computer Graphics, vol. 1, no. 2, pp. 99–108, 1995.
[22]
R. M. Haralick and L. G. Shapiro, “Image segmentation techniques.,” Computer Vision, Graphics, & Image Processing, vol. 29, no. 1, pp. 100–132, 1985.
[23]
I. Carlbom, D. Terzopoulos, and K. M. Harris, “Computer-assisted registration, segmentation, and 3D reconstruction from images of neuronal tissue section,” IEEE Transactions on Medical Imaging, vol. 13, no. 2, pp. 351–362, 1994.
[24]
B. S. Kuszyk, D. R. Ney, and E. K. Fishman, “The current state of the art in three dimensional oncologic imaging: an overview,” International Journal of Radiation Oncology Biology Physics, vol. 33, no. 5, pp. 1029–1039, 1995.
[25]
S. Zimeras and G. Karangelis, “Semi-automatic segmentation techniques for CT medical data,” in Proceedings of the 3rd caesarium Computer Aided Medicine, Bonn, Germany, November, 2001.
[26]
G. Karangelis, S. Zimeras, E. Firle, Min Wang, and G. Sakas, “Volume definition tools for medical image applications,” in Proceedings of the 4th MICCAI International Conference, M.-A. Viergever, T. Dohi, and M. Vannier, Eds., vol. 2208 of Lecture Notes in Computer Sciences, pp. 1295–1297, Springer, Utrecht, The Netherlands, 2001.
[27]
S. Zimeras and G. Karangelis, “An efficient 3D shape reconstruction techniques for CT images using volume definition tools,” in Proceedings of the ICCMSE, vol. 4 of Lecture Series on Computer and Computational Sciences, pp. 1532–1529, 2005.
[28]
G. Karangelis and S. Zimeras, “An accurate 3D segmentation method of the spinal canal applied on CT images,” in Proceedings of the Bildverarbeitung für die Medizin (BVM '02), M. Meiler, D. Saupe, F. Kruggel, H. Handels, and T. M. Lehmann, Eds., pp. 366–369, Springer, Berlin, Germany, 2002.
[29]
G. Karangelis and S. Zimeras, “3D segmentation method of the spinal cord applied on CT data,” Computer Graphics Topics, vol. 14, pp. 28–29, 2002.
[30]
G. Wolberg, Digital Image Warping, chapter, Wiley-IEEE Press, New York, NY, USA, 1st edition, 1990.
[31]
R. C. Gonzales and R. E. Woods, Digital Image Processing, Addison-Wesley, Boston, Mass, USA, 1992.
[32]
G. Karangelis and S. Zimeras, “Segmentation of anatomical structures using volume definition tools,” in Proceedings of the European Computing Conference, vol. 27 of Lecture Notes in Computer Science, no. 7, pp. 767–777, Springer, Athens Greece, September 2007.
[33]
I. A. Sachpazidis, R. Ohl, C. A. Polanczyk et al., “Applying telemedicine to remote and rural underserved regions in Brazil using eMedical consulting tool,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 2191–2195, Shanghai, China, September 2005.
