Ischemic Stroke Enhancement in Computed Tomography scans using a computational approach
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In this work, a novel approach was proposed to enhance the visual perception of ischemic stroke in computed tomography scans. Through different image processing techniques, we enabled less experienced physicians, to reliably detect early signs of stroke. A set of 40 retrospective CT scans of patients were used, divided into two groups: 25 cases of acute ischemic stroke and 15 normal cases used as control group. All cases were obtained within 4 hours of symptoms onset. Our approach was based on the variational decomposition model and three different segmentation methods. A test determined observers' performance to correctly diagnose stroke cases. The Expectation Maximization method provided the best results among all observers. The overall sensitivity of the observer's analysis was 64% and increased to 79%. The overall specificity was 67% and increased to 78%. These results show the importance of a computational tool to assist neuroradiology decisions, especially in critical situations such as the diagnosis of ischemic stroke.
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