Toward an efficient brain tumor extraction using level set method and pennes bioheat equation

The aim of this paper is to present a new approach for improving deformable models and especially the Level Set Method (LSM) for segmentation and extraction of brain tumors in MRI (Magnetic Resonance Imaging) with more accuracy, the contribution of this work is to exploit thermal behavior of brain tumors for correcting level set contours. Human Body temperature distribution is an indicator of health condition, the brain tumor cells generate more heat than normal brain cells due to their higher metabolism and their vascular dilation. Heat distribution in human body is modeled using Pennes BioHeat Transfer Equation (PBHTE) solved by Finite Volume Method (FVM), and with the inverse analysis using Genetic Algorithm (GA) we will estimate the size and location of brain tumor, with this way Level Set Method extracts tumors contours with more accuracy and efficiency. To our knowledge, this is the first approach which introduces thermal analysis to improve the accuracy of segmentation and extraction of tumors in MRI images.