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

DOI: 10.4236/jbise.2011.43026   PDF   HTML     4,113 Downloads   8,081 Views   Citations


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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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.

Conflicts of Interest

The authors declare no conflicts of interest.


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