IDENTIFY BRAIN TUMORS IN 2 D MRI IMAGES USING FAST BOUNDING BOX METHOD

Image segmentation is a problem in computer vision and is of more important than anything else for medical imaging. For most subsequent image analysis task, medical image segmentation is an important step. In neuro imaging analysis, the segmentation of an atomic structure for brain plays a crucial role. It involves accurate tissue segmentation of brain magnetic resonance (MR) images by the study of many brain disorders. It is lacking variety by an human expert for studies of involving larger databases in manual segmentation of the brain tissues like white matter, gray matter and cerebrospinal fluid in MR images. By the overlapping of MR intensities of many different tissue classes and by the presence of a spatially and smoothly varying intensity makes the segmentation much complicated. The main objective of this study is to develop strategies and methodologies for identifying the brain tumors in 2 d MRI images using automated
approaches. It is a challenging task in MRI for accurately detection and segmentation of brain tumor. The MRI image is a type of image that produces a high contrast images which indicates regular and irregular tissues which also helps to distinguish the overlapping of each limb in margin. Fast bounding box method is used for better detection of cancer. MATLAB Simulink, is used for the simulation