Brain tumor segmentation using DE embedded OTSU method and neural network

In the past few decades, medical imaging and soft computing have shown a symbolic growth in brain tumor segmentation. Research in medical imaging is becoming quite popular field, particularly in magnetic resonance images of brain tumor, because of the tremendous need of efficient and effective technique for evaluation of large amount of data. Image segmentation is considered as one of the most crucial techniques for visualizing tissues in human being. In considering brain tumor image segmentation, manually with an expert, it is more likely that the errors are present in it. To automate image segmentation, we have proposed an algorithm to obtain a global thresholding value for a particular image. To find out an optimal threshold value we have used Differential Evolution algorithm embedded with OTSU method and trained neural network for future use. Proposed Methodology provides classification of the images successfully for brain tumors. Results show its efficiency over other methods.