AI Article Synopsis
- Researchers suggest a new technique for locating objects in fluorescent tomography (FT) that effectively handles complex, non-uniform backgrounds.
- Traditional methods fail in heterogeneous environments because they assume a uniform background, limiting their accuracy.
- The proposed algorithm uses an iterative approach to separate background and object data, showing promising results in simulations for improved localization in challenging conditions.
Article Abstract
We propose a method for object localization in fluorescent tomography (FT) in the presence of a highly heterogeneous background. Existing approaches typically assume a homogeneous background distribution; thus, they are incapable of accurately accounting for the more general case of an unconstrained, possibly heterogeneous, background. The proposed method iteratively solves the inverse problem over a solution space partitioned into a background subspace and an object subspace to simultaneously estimate the background and localize the target fluorescent objects. Simulation results of this algorithm applied to continuous-wave FT demonstrate effective localization of target objects in the presence of highly heterogeneous background distributions.
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| http://dx.doi.org/10.1364/josaa.25.001467 | DOI Listing |
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