An ischemic stroke (IS) naturally originates with rapid onset neurological shortfall, which can be verified by analyzing the internal regions of brain. Computed tomography (CT) and magnetic resonance image (MRI) are the commonly used non-invasive medical examination techniques used to record the brain abnormalities for clinical study. In order to have a pre-opinion regarding the brain abnormality in clinical level, it is essential to use a suitable image processing tool to appraise the digital CT/MR images. In this chapter, a hybrid image processing technique based on the social group optimization assisted Tsallis entropy and watershed segmentation (WS) is proposed to examine ischemic stroke region from digital CT/MR images. For the experimental study, the digital CT/MRI datasets like Radiopedia, BRATS-2013, and ISLES-2015 are considered. Experimental result of this study confirms that, proposed hybrid approach offers superior results on the considered image datasets.
An ANN Based Brain Abnormality Detection Using MR Images
