scholarly journals Real-time maximum a-posteriori image reconstruction for fluorescence microscopy

AIP Advances ◽  
2015 ◽  
Vol 5 (8) ◽  
pp. 084801 ◽  
Author(s):  
Anwar A. Jabbar ◽  
Shilpa Dilipkumar ◽  
Rasmi C K ◽  
K. Rajan ◽  
Partha P. Mondal
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Real Time ◽  
Maximum A Posteriori ◽  
A Posteriori

Multispectral Bayesian reconstruction technique for real-time two color fluorescence microscopy

RSC Advances ◽  
2015 ◽  
Vol 5 (17) ◽  
pp. 13175-13183 ◽  
Author(s):  
Shilpa Dilipkumar ◽  
Ravi Manjithaya ◽  
Partha Pratim Mondal
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Real Time ◽  
Spectral Imaging ◽  
Reconstruction Technique ◽  
Imaging Method ◽  
Combined Approach ◽  
Bayesian Reconstruction ◽  
Image Reconstruction Technique ◽  
Bayesian Image Reconstruction

We have developed a real-time imaging method for two-color widefield fluorescence microscopy using a combined approach that integrates multi-spectral imaging and Bayesian image reconstruction technique.

scholarly journals Bayesian Maximum-A-Posteriori Approach with Global and Local Regularization to Image Reconstruction Problem in Medical Emission Tomography

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1108
Author(s):  
Natalya Denisova
Keyword(s):  
Image Reconstruction ◽  
Single Photon ◽  
Photon Emission ◽  
Reconstruction Algorithm ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Local Regularization ◽  
Global And Local ◽  
Prior Models ◽  
Global Regularization

The Bayesian approach Maximum a Posteriori (MAP) provides a common basis for developing statistical methods for solving ill-posed image reconstruction problems. MAP solutions are dependent on a priori model. Approaches developed in literature are based on prior models that describe the properties of the expected image rather than the properties of the studied object. In this paper, such models have been analyzed and it is shown that they lead to global regularization of the solution. Prior models that are based on the properties of the object under study are developed and conditions for local and global regularization are obtained. A new reconstruction algorithm has been developed based on the method of local statistical regularization. Algorithms with global and local regularization were compared in numerical simulations. The simulations were performed close to the real oncologic single photon emission computer tomography (SPECT) study. It is shown that the approach with local regularization produces more accurate images of ‘hot spots’, which is especially important to tumor diagnostics in nuclear oncology.

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