Abstract
Automated brain MR image segmentation is a challenging problem and received significant attention lately. Several improvements have been made to the standard fuzzy c-means (FCM) algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. In this paper we present a modified FCM algorithm, which aims accurate segmentation in case of mixed noises, and performs at a high processing speed. The proposed method extracts a scalar feature value from the neighborhood of each pixel, using a filtering technique that deals with both spatial and gray level distances. These features are classified afterwards using the histogram-based approach of the enhanced FCM classifier. The experiments using synthetic phantoms and real MR images show, that the proposed method provides better results compared to other reported FCM-based techniques.
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Szilágyi, L., Szilágyi, S.M., Benyó, Z. (2007). A Modified FCM Algorithm for Fast Segmentation of Brain MR Images. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_13
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DOI: https://doi.org/10.1007/978-3-540-72432-2_13
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