Segmentation and Classification of Brain MR Images Using Big Data Analytics

Abstract

Medical imaging is a field of technology which is mainly used to get referential images of human body for medical science and research work. These images are extremely useful for us. We can detect brain tumor in a human body with the help of these MRI Images collected as Big Data. The Automation in this particular area is one of the hot topics of research in Medical Science. These images help us in determining the exact location and area of the weird tissues. Its orientation can also be found out. We can use support vector machine (SVM) for classification, for brain tumor segmentation use fuzzy c-means(FCM) and discrete wavelet transform(DWT). To detect the suspicious region in brain MRI image we need to do clustering and then the features are extracted from the brain image. Feature extraction is a method of capturing visual content of an image. The MRI Images having tumor are downloaded from the internet and the experiment is done on them. After which the SVM technique is used for the classifying the MRI images of brain, which in turn gives a more efficient result with high accuracy for brain MRI image classification and finally the accuracy of both the segmentation algorithms is compared.