Active mask segmentation of fluorescence microscope images.
We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.
Srinivasa
Gowri
G
Department of Information Science and Engineering and the Center for Pattern Recognition, PES School of Engineering, Bangalore, India.
Fickus
Matthew C
MC
Guo
Yusong
Y
Linstedt
Adam D
AD
Kovacević
Jelena
J
D000465:Algorithms; D003584:Cytological Techniques / Q000379:methods; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*