Short Description
The C-CRAFT software enables to segment particles and estimate background in 2D or 3D image sequences. We consider a statistical Bayesian approach in the framework of conditional random fields. Within this approach, we take advantage of a robust detection measure for fluorescence microscopy based on the distribution of neighbor patch similarity. We formulate the vesicle segmentation and background estimation as a global energy minimization problem. An iterative scheme to jointly segment vesicles and background is proposed for 2D-3D fluorescence image sequences.Background Fluorescence Estimation and Vesicle Segmentation in Live Cell Imaging With Conditional Random Fields: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6983606