Level Set Based Segmentation of Cell Nucleus in Fluorescence Microscopy Images Using Correntropy-Based K-Means Clustering
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.
2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Artificial Intelligence and Image Processing not elsewhere classified