This project propose a deep learning neural network by using brain tumor segmentation from Magnetic Resonance Imaging (MRI) is of great importance for better tumor diagnosis, growth rate prediction and radiotherapy planning. But this task is extremely challenging due to intrinsically heterogeneous tumor appearance, the presence of severe partial volume effect and ambiguous tumor boundaries. In this work, a unique approach of tumor segmentation is introduced based on super pixel level features extracted from all three planes (x-y, y-z, and z-x) of 3D volumetric MR images. In order to avoid the pixel randomness and to account for precise inhomogeneous boundaries of brain tumor, each of the images belonging to a particular plane is partitioned into irregular patches (super pixels) based on their intensity and spatial similarity. Next, various statistical and textural features are extracted from each super pixel where all three planes are considered separately in order to obtain better labeling on super pixels in tumor edges. A feature selection scheme is proposed based on their performance on histogram based consistency analysis and local descriptor pattern analysis, which offers a significant reduction in feature dimension without sacrificing classification performance. For the purpose of supervised classification, Extremely Randomized Trees is used to classify these super pixels into a tumor or a non-tumor class. Finally, pixel level decision is taken based on corresponding decisions obtained in each plane. Extensive simulations are carried out on publicly available dataset and it is found that the proposed method offers better tumor segmentation performance in comparison to that obtained by some state of the art methods. This project is implemented with MATLAB software.