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- W2017003967 abstract "Introduction of many Image processing and segmentation tools has undoubtedly presented the procedures of mapping the human brain in a more efficient way. This paper attempts to pull out a new and a practical approach for enhancing the underlying delicate architectures of the human brain images captured by a Magnetic Resonance Imaging(MRI) machine in a much better way. Edge detection is a fundamental tool for the basic study of human brain particularly in the areas of feature detection and feature extraction. The edge detection methodology presented in this paper relies on two basic stages: Firstly, the original MRI image is subjected to image segmentation which is done using Particle Swarm optimization incorporating Fuzzy C Means Clustering (PSOFCM) technique. And secondly, canny edge detection algorithm is used for detecting the fine edges. After implementation it was found that this technique yields better edge detected image of the human brain as compared to other edge detection methods as discussed below." @default.
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- W2017003967 date "2014-01-01" @default.
- W2017003967 modified "2023-10-18" @default.
- W2017003967 title "A Novel MRI Brain Edge Detection Using PSOFCM Segmentation and Canny Algorithm" @default.
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- W2017003967 doi "https://doi.org/10.1109/icesc.2014.78" @default.
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