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.

[1]  Chunming Li,et al.  A Variational Level Set Approach to Segmentation and Bias Correction of Images with Intensity Inhomogeneity , 2008, MICCAI.

[2]  Khaled Khairy,et al.  Detection of Deformable Objects in 3D Images Using Markov-Chain Monte Carlo and Spherical Harmonics , 2008, MICCAI.

[3]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Tony F. Chan,et al.  An Active Contour Model without Edges , 1999, Scale-Space.

[5]  Gabor Fichtinger,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I , 2008, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[6]  Christophe Zimmer,et al.  Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces , 2005, IEEE Transactions on Image Processing.

[7]  Claudia Redenbach,et al.  Microstructure models for cellular materials , 2009 .

[8]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[9]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[10]  Joel H. Saltz,et al.  Tensor classification of N-point correlation function features for histology tissue segmentation , 2009, Medical Image Anal..