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Comparison of SPECT cerebral images examination methods based on luminance level and morphological spectra evaluation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
It is presented a method of SPECT (single-photon emission tomography) cerebral images examination based on morphological spectra. The advantages of the SPECT imaging in early diagnosing of encephalic diseases are emphasized. The detected radiation levels in the SPECT imaging are visualized by luminance levels which give insight into the lesions of cerebral tissue. It is shown that a rough, on luminance level based, examination of the SPECT images can be improved if more sophisticated analytical methods are used. Basic notions and properties of morphological spectra and their applicability as tools for biomedical image analysis are shortly reminded. A simple formula for reversing transformation reconstructing of original image on the basis of a given morphological spectrum is presented. Results of experiments consisting in comparison of the morphological spectra calculated for selected pairs of testing windows in the SPECT cerebral images are shown. It has been shown that the morphological spectra can better suit to an effective comparison of views of the cerebral regions located symmetrically with respect to the brain axis separating the left and right cerebral hemispheres than the averaged luminance level.
Twórcy
autor
autor
  • Instytut Biocybernetyki i Inżynierii Biomedycznej PAN, 02-109 Warszawa, ul. Trojdena 4
Bibliografia
  • 1. Israel O., Goldsmith S.J. (Eds.): Hybrid SPECT/CT Imaging in Clinical Practice. Taylor & Francis, New York 2006.
  • 2. Rumiński J., Kalicka R., Bobek-Billewicz B.: Parametric Imaging in brain investigation by MRI/PET methods, Wyd. Gdańskie, Sp.z.o.o., Gdańsk 2006, (in Polish).
  • 3. Leemput K. Van., Vandermeulen D., Maes F., Srivastava S., D'Agostino E., Suetens P.: Model-Based Brain Tissue Classification. In: J.S. Suri, D.L. Wilson, S. Laxminarayan (Eds.): Handbook of Biomedical Image Analysis. Vol. I, Segmentation Models, Part B. Kluwer Academic/Plenum Publishers, New York 2005, 1-45.
  • 4. Bruno A., Collorec R., Bezy-Wendling J. et al.: Texture Analysis in Medical Imaging. In: Roux C., Coatrieux J.-L. (Eds.), Contemporary Perspectives in Three-Dimensional Biomedical Imaging. IOS Press, Amsterdam 1997.
  • 5. Haddon J.F., Boyce J.F.: Texture Segmentation and Region Classification by Orthogonal Decomposition of Cooccurrence Matrices. Proc. 11th IAPR Intern. Confer. on Pattern Recognition, Hague, IEEE Computer Society Press, Los Alamitos l 992, 692-695.
  • 6. Ojala T., Pietikajnen M.: Unsupervised Texture Segmentation Using Feature Distributions. Texture Analysis Using Pairwise Interaction Maps. Image Analysis and Processing, 9th Intern. Confer., ICIAP'97, Proc. (A. Del Bimbo ed.), Florence 1997,I, 311-318.
  • 7. Farag A.A., Ahmed M.N., El;-Baz A., Hassan H.: Advanced Segmentation Techniques. In: J.S. Suri, D.L. Wilson, S. Laxminarayan (Eds.) Handbook of Biomedical Image Analysis. Vol. I, Segmentation Models, Part A. Kluwer Academic/Plenum Publishers, New York 2005, 479-533.
  • 8. Yang S., Mitra S.: Statistical and Adaptive Approaches for Optimal Segmentation in Medical Images. In: Suri J.S., Wilson D.L., Laxminarayan S. (Eds.) Handbook of Biomedical Image Analysis. Vol. I, Segmentation Models, Part B. Kluwer Academic/Plenum Publishers, New York 2005, 267-314.
  • 9. Przytulska M., Kulikowski J.L., Bajera A.: A Comparative Analysis of SPECT Images of the Left and Right Cerebral Hemispheres in Patients with Diagnosed Epileptic Symptoms. In: E. Kącki (Ed.) Computers in Medical Activity, (to occur), 2008.
  • 10. Olejarczyk E., Przytulska M., Bajera A., Królicki L.: Comparative Analysis of the SPECT Images of the Left and Right Cerebral Hemispheres in Patients with Epileptic Symptoms. Biocybernetics and Biomedical Engineering, 2008, 28 (to occur).
  • 11. Kulikowski J.L., Przytulska M., Wierzbicka D.: Recognition of Textures Based on Analysis of Multilevel Morphological Spectra. IFMBE Proceedings, World Congress, Seoul 2006, 14, 2164-2167.
  • 12. Kulikowski J.L., Przytulska M., Wierzbicka D.: Morphological Spectra as Tools for Texture Analysis. In: Kurzynski M. et al. (Eds.), Computer Recognition Systems 2 . Advances in Soft Computing 45, Springer, Berlin 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ3-0030-0012
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