Markov random field modeling in image analysis
Markov random field modeling in image analysis
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Metrics Can Be Approximated by Geo-Cuts, Or Global Optimization of Length/Area and Flux
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Shape Representation and Classification Using the Poisson Equation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification
Proceedings of the 30th DAGM symposium on Pattern Recognition
Variational Curve Skeletons Using Gradient Vector Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Carving: scalable interactive segmentation of neural volume electron microscopy images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
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Understanding neural connectivity and structures in the brain requires detailed three-dimensional (3D) anatomical models, and such an understanding is essential to the study of the nervous system. However, the reconstruction of 3D models from a large set of dense nanoscale microscopy images is very challenging, due to the imperfections in staining and noise in the imaging process. To overcome this challenge, we present a 3D segmentation approach that allows segmenting densely packed neuronal structures. The proposed algorithm consists of two main parts. First, different from other methods which derive the shape prior in an offline phase, the shape prior of the objects is estimated directly by extracting medial surfaces from the data set. Second, the 3D image segmentation problem is posed as Maximum A Posteriori (MAP) estimation of Markov Random Field (MRF). First, the MAPMRF formulation minimizes the Gibbs energy function, and then we use graph cuts to obtain the optimal solution to the energy function. The energy function consists of the estimated shape prior, the flux of the image gradients, and the gray-scale intensity. Experiments were conducted on synthetic data and nanoscale image sequences from the Serial Block Face Scanning Electron Microscopy (SBFSEM). The results show that the proposed approach provides a promising solution to EM reconstruction. We expect the reconstructed geometries to help us better analyze and understand the structure of various kinds of neurons.