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

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DOI: 10.4236/jbise.2011.43026    3,971 Downloads   7,967 Views   Citations
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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.

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The authors declare no conflicts of interest.

Cite this paper

Shibli, M. (2011) In Vivo Dynamic Image Characterization of Brain Tumor Growth Using Singular Value Decomposition and Eigenvalues. Journal of Biomedical Science and Engineering, 4, 187-195. doi: 10.4236/jbise.2011.43026.


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