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Article citations


Ashino, R., Morimoto, A., Nagase, M. and Vaillancourt, R. (2003) Comparing multiresolution SVD with other methods for image compression. Proceedings of the 4th International ISAAC Congress, York University, Toronto, 11-16 August 2003, 457-470.

has been cited by the following article:

  • TITLE: In Vivo Dynamic Image Characterization of Brain Tumor Growth Using Singular Value Decomposition and Eigenvalues

    AUTHORS: Murad Shibli

    KEYWORDS: Brain cancer, Tumor Image Identification, Singular Value Decomposition

    JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.4 No.3, March 8, 2011

    ABSTRACT: This paper presents a dynamic image approach to characterize the growth of brain cancer invasion of tumor gliomas cells using singular value decomposi-tion (SVD) technique. Such a dynamic image is identi-fied by the white and grey matter displayed by mag-netic resonance (MR) images of the patient brain taken at different times. SVD components and prop-erties have been analyzed for different brain images. It is figured out that the growth of tumor cells is quantized by the SVD eigenvalues. Since SVD geo-metrically interprets an ellipsoid transformation, then the higher the eigenvalues, the more of tumor growth is. In vivo SVD dynamic imaging offers a more pre-dictive model to assess the tumor therapy than con-ventional technologies. Furthermore, an efficient dy-namic white-black indicator of the tumor growth rate is constructed based on the change in the diagonal eigenvalues matrices of two MR images taken at dif-ferent times. Finally, SVD image processing results are demonstrated to verify the effectiveness of the applied approach that can be implemented for each individual patient.