Name
Abstract | ||
|---|---|---|
Despite major advances in x-ray sources, detector arrays, gantry mechanical design and special computer performances, computed tomography (CT) enjoys the filtered back projection (FBP) algorithm as its first choice for the CT image reconstruction in the commercial scanners [1]. Over the years, a lot of fundamental work has been done in the area of finding the sophisticated solutions for the inverse problems using different kinds of optimization techniques. Recent literature in applied mathematics is being dominated by the compressive sensing techniques and/or sparse reconstruction techniques [2], [3]. Still there is a long way to go for translating these newly developed algorithms in the clinical environment. The reasons are not obvious and seldom discussed [1]. Knowing the fact that the filtered back projection is one of the most popular CT image reconstruction algorithms, one pursues research work to improve the different error estimates at different steps performed in the filtered back projection. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.912272 | IMAGE PROCESSING: ALGORITHMS AND SYSTEMS X AND PARALLEL PROCESSING FOR IMAGING APPLICATIONS II |
Keywords | Field | DocType |
image restoration,mathematics,compressed sensing,applied mathematics,tomography,wavelets,algorithms,computed tomography,convolution,inverse problems,mechanical engineering | Iterative reconstruction,Computer vision,Convolution,Optics,Tomography,Artificial intelligence,Inverse problem,Image restoration,Radon transform,Approximation error,Wavelet,Physics | Conference |
Volume | ISSN | Citations |
8295 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Challa S. Sastry | 1 | 65 | 9.51 |
| s k singh | 2 | 0 | 0.34 |