Improved Brain Tumor Detection Using Object Based Segmentation

This paper has focused on the brain tumor detection techniques. The brain tumor detection is a very important vision application in the medical field. This work has firstly presented a review on various well known techniques for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main objective of the work is to explore various techniques to detect brain tumor in an efficient way. It has been found that the most of existing methods has ignored the poor quality images like images with noise or poor brightness. Also the most of the existing work on tumor detection has neglected the use of object based segmentation. So to overcome the limitations of earlier work a new technique has been proposed in this research work. The technique has shown quite effective results than neural based tumor detection technique. The design and implementation of the proposed algorithm is done in MATLAB using image processing toolbox. The comparison has shown that the proposed technique has achieved up to 94 % accuracy which was 78 % in neural based technique. Also for high corrupted noisy images the proposed technique has shown quite effective results than the neural based tumor detection.