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.
Highlights
A brain tumor is defined as an intracranial solid neoplasm within the brain or the central spinal canal
This paper presents a dynamic image approach to characterize the growth of brain cancer invasion of tumor gliomas cells using singular value decomposition (SVD) technique
Such a dynamic image is identified by the white and grey matter displayed by magnetic resonance (MR) images of the patient brain taken at different times
Summary
A brain tumor is defined as an intracranial solid neoplasm within the brain or the central spinal canal. The quantification process was formulated by an inverse problem and solved using anisotropic fast marching method yielding an efficient algorithm It was tested on a few images to get a first proof of concept with promising results. To assess the value of pelvic-phased array (PPA) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting intraprostatic tumour location and volume for clinically localized prostate cancers in [5]. A mathematical equation for superiority value was developed for comparison of the key ratios of the image-processing combinations. In this proposed paper SVD components and properties have been analyzed for different brain images.
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