A Multi-Kernel Local Level Set Image Segmentation Algorithm for Fluorescence Microscopy Images
Fluorescence microscopy image segmentation is a central task in high-throughput applications such as protein expression quantification and cell function investigation. In this paper, a multiple kernel local level set segmentation algorithm is introduced as a framework for fluorescence microscopy cell image segmentation. In this framework, a new local region-based active contour model in a variational level set formulation based on the piecewise constant model and multiple kernels mapping is proposed where a linear combination of multiple kernels is utilized to implicitly map the original local image data into data of a higher dimension. We evaluate the performance of the proposed method 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)
Pattern Recognition and Data Mining