Name
Abstract | ||
|---|---|---|
We introduce a tomographic reconstruction method implemented using a shape-based regularization technique. Spatial models of known features in the structure being reconstructed are integrated into the reconstruction process as regularizers. Our regularization scheme is driven locally through shape information obtained from segmentation and compared with a known spatial model. We demonstrated our method on tomography data from digital phantoms, simulated data, and experimental electron tomography (ET) data of virus complexes. Our reconstruction showed reduced blurring and an improvement in the resolution of the reconstructed volume was also measured. This method also produced improved demarcation of spike boundaries in viral membranes when compared with popular techniques like weighted back projection and the algebraic reconstruction technique. Improved ET reconstructions will provide better structure elucidation and improved feature visualization, which can aid in solving key biological issues. Our method can also be generalized to other tomographic modalities. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TMI.2012.2214229 | Medical Imaging, IEEE Transactions |
Keywords | Field | DocType |
biomembranes,cellular biophysics,image reconstruction,image resolution,image restoration,image segmentation,medical image processing,microorganisms,phantoms,tomography,ET data,ET reconstructions,digital phantoms,electron tomographic reconstruction method,experimental electron tomography data,feature visualization,image deblurring,image segmentation,reconstructed volume,shape information,shape-based regularization technique,simulated data,spatial model,spike boundaries,viral membranes,virus complexes,Bayesian methods,electron microscopy,reconstruction,shape-based regularization,tomography | Iterative reconstruction,Computer vision,Tomographic reconstruction,Electron tomography,Electron Microscope Tomography,Image segmentation,Tomography,Regularization (mathematics),Algebraic Reconstruction Technique,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
31 | 12 | 0278-0062 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ajay Gopinath | 1 | 11 | 2.14 |
| Guoliang Xu | 2 | 205 | 13.03 |
| David Ress | 3 | 0 | 0.34 |
| Ozan Öktem | 4 | 53 | 7.18 |
| Subramaniam Sriram | 5 | 64 | 5.84 |
| Chandrajit L. Bajaj | 6 | 2880 | 306.59 |