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- W2016208899 abstract "The detection of cerebral aneurysms is of a paramount importance in the prevention of intracranial sub- arachnoid hemorrhage. The segmentation of intracranial vascu- lature presents a crucial step in the detection scheme. We propose in this paper, a new approach to extract cerebral vasculature in 2D-DSA images based on multiple classifier fusion. The classifiers used here are the FCM and the Fuzzy KNN. The main advantage of multiple classifier fusion is increasing classification efficiency and accuracy. The proposed method demostrates the contribution of fusing FCM-FKNN over the use of the individual classifier. Our method succeeded in classifying 6.23% of pixels rejected by the FCM method and 8.36% of pixels rejected by the FKNN method. Keywords—Cerebral vasculature segmentation, Angiographic images, Classifier fusion, FCM, FKNN." @default.
- W2016208899 created "2016-06-24" @default.
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- W2016208899 date "2014-10-01" @default.
- W2016208899 modified "2023-09-26" @default.
- W2016208899 title "Cerebral vasculature extraction using classifier fusion" @default.
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- W2016208899 doi "https://doi.org/10.1109/ist.2014.6958503" @default.
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