Detection of spatially correlated objects in 3D images using appearance models and coupled active contours.
We consider the problem of segmenting 3D images that contain a dense collection of spatially correlated objects, such as fluorescent labeled cells in tissue. Our approach involves an initial modeling phase followed by a data-fitting segmentation phase. In the first phase, cell shape (membrane bound) is modeled implicitly using a parametric distribution of correlation function estimates. The nucleus is modeled for its shape as well as image intensity distribution inspired from the physics of its image formation. In the second phase, we solve the segmentation problem using a variational level-set strategy with coupled active contours to minimize a novel energy functional. We demonstrate the utility of our approach on multispectral fluorescence microscopy images.
Mosaliganti
Kishore
K
Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. kishore@hms.harvard.edu
Gelas
Arnaud
A
Gouaillard
Alexandre
A
Noche
Ramil
R
Obholzer
Nikolaus
N
Megason
Sean
S
D000465:Algorithms; D002467:Cell Nucleus / Q000648:ultrastructure*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity