A Segmentation and Classification Techniques for MRI Brain Tumor Images

Abstract

Brain tumor is among the most frequent cancers and its mortality rate is very high. The brain tumor segmentation problem is used to distinguish tumor tissues such as edema and active tumor from normal brain tissues such as gray matter, white matter and the cerebrospinal fluid. To propose a brain tumor segmentation method from multi-sequence images. The method selects the most relevant features and segments edema and tumor using a classification algorithm based on Multiple Kernel Learning (MKL). Using MKL algorithm, we can associate one or more kernels to each feature. Each kernel is associated to a weight reflecting its importance in the classification. A sparsity constraint on the kernel weights allows to force same weights to be equal to zero corresponding to insignificant kernels (non- informative features).Feature selection and dictionary learning in image segmentation are usually combined with RUSBOOST classifier for identifying the tumor. The RF classifier has increased the classification accuracy as evident by quantitative results of our proposed method which are comparable or higher than the state of the art