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• W2247273808 abstract "Fluorescence microscopy image segmentation is a challenging task in fluorescence microscopy image analysis and high-throughput applications such as protein expression quantification and cell function investigation. In this paper, a novel local level set segmentation algorithm in a variational level set formulation via a correntropy-based k-means clustering (LLCK) is introduced for fluorescence microscopy cell image segmentation. The performance of the proposed method is evaluated using a large number of fluorescence microscopy images. A quantitative comparison is also performed with some state-of-the-art segmentation approaches." @default.
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• W2247273808 date "2015-11-01" @default.
• W2247273808 modified "2023-09-26" @default.
• W2247273808 title "Level Set Based Segmentation of Cell Nucleus in Fluorescence Microscopy Images Using Correntropy-Based K-Means Clustering" @default.
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• W2247273808 doi "https://doi.org/10.1109/dicta.2015.7371279" @default.
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