Abstract
In present scenario accurate detection of cerebral aneurysms in medical images plays a crucial role in reducing the incidents of subarachnoid hemorrhage (SAH) which carries a high rate of mortality. Many of the non-traumatic SAH cases are caused by ruptured cerebral aneurysms and accurate detection of these aneurysms can decrease a significant proportion of misdiagnosed cases. A scheme for automated detection of cerebral aneurysms is proposed in this study. The aneurysms are found by applying Normalization and generating the Probability Density Function (PDF) for the input image, local thresholding is used to identify appropriate aneurysm candidate regions. Feature vectors are calculated for the candidate regions based on gray-level, morphological and location based features. Rule based system is used to classify and detect cerebral aneurysms from candidate regions. Accuracy of the system is calculated using the sensitivity parameter.
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Vaseemahamed, M., Ravishankar, M. (2015). Automated System for Detection of Cerebral Aneurysms in Medical CTA Images. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-12012-6_59
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DOI: https://doi.org/10.1007/978-3-319-12012-6_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12011-9
Online ISBN: 978-3-319-12012-6
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