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Abstract #2888

A Multi-Strategy Method for MRI Segmentation

Miguel Martin-Landrove1,2, Gabriel Padilla3, Giovanni Figueroa3, Marco Paluszny3,4, Wuilian Torres3,5

1Center for Molecular and Medical Physics, Universidad Central de Venezuela, Caracas, Venezuela; 2Centro de Diagnstico Docente, Las Mercedes, Caracas, Venezuela; 3Center of Geometry, Universidad Central de Venezuela, Caracas, Venezuela; 4Department of Mathematics, Universidad Nacional de Colombia, Medelln, Colombia; 5Fundacin Instituto de Ingeniera, FII, Caracas, Venezuela

A method for T2-weighted MRI segmentation according to tissue transversal magnetization decay rates is presented. A log-convexity filter is applied first to regularize image intensity decay and control noise, followed by curve fitting with de Prony pseudo-interpolating and Montecarlo-Vandermonde robustness filters. Decay rate distributions and decay modes are obtained for tissue classification. Linear regression analysis is used for image segmentation assuming that pixel intensity decay is a linear superposition of decay modes. The proposed approach shows accuracy and computational speed compared to Inverse Laplace Transform or Vandermonde like methods. It can be extended to other images such as diffusion-weighted MRI.