Abstract
Medical image analysis plays a vital role in the diagnosis and prognosis of brain-related diseases. MR images are often preferred for brain anatomy analysis for their high resolution. In this work, the components of the brain are analyzed to identify and locate the region of interest (hippocampus). The internal structures of the brain are segmented via the combination of wavelet and watershed approach. The segmented regions are categorized through semantic categorization. The region of interest is identified and cropped, and periodical volume analysis is performed to identify the atrophy. The atrophy detection of the proposed system is found to be more effective than the identification done by the traditional system of radiologist. Performance measures such as sensitivity, specificity, and accuracy are used to evaluate the system.
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Selva Bhuvaneswari, K., Geetha, P. (2014). Hippocampus Atrophy Detection Using Hybrid Semantic Categorization. In: Krishnan, G., Anitha, R., Lekshmi, R., Kumar, M., Bonato, A., Graña, M. (eds) Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol 246. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1680-3_11
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DOI: https://doi.org/10.1007/978-81-322-1680-3_11
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