Image processing and image reconstruction with the use of a-priori information

1990 ◽  
Author(s):  
Chin-Tu Chen ◽  
Valen E. Johnson ◽  
Xiaoping Hu ◽  
Wing H. Wong ◽  
Charles E. Metz
Keyword(s):  
Image Processing ◽  
Image Reconstruction ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information

Using Deconvolution to Improve PET Spatial Resolution in OSEM Iterative Reconstruction

2007 ◽  
Vol 46 (02) ◽  
pp. 231-235 ◽  
Author(s):  
I. Castiglioni ◽  
G. Russo ◽  
M. Tana ◽  
F. Dell'Acqua ◽  
M. Gilardi ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Spatial Resolution ◽  
Penalty Function ◽  
Iterative Reconstruction ◽  
A Priori ◽  
Image Deconvolution ◽  
A Priori Information ◽  
Novel Approach ◽  
Priori Information ◽  
Pet Image Reconstruction

Summary Objectives : A novel approach to the PET image reconstruction is presented, based on the inclusion of image deconvolution during conventional OSEM reconstruction. Deconvolution is here used to provide a recovered PET image to be included as “a priori" information to guide OSEM toward an improved solution. Methods : Deconvolution was implemented using the Lucy-Richardson (LR) algorithm: Two different deconvolution schemes were tested, modifying the conventional OSEM iterative formulation: 1) We built a regularizing penalty function on the recovered PET image obtained by deconvolution and included i in the OSEM iteration. 2) After each conventional global OSEM iteration, we deconvolved the resulting PET image and used this “recovered" version as the initialization image for the next OSEM iteration. Tests were performed on both simulated and acquired data. Results : Compared to the conventional OSEM, both these strategies, applied to simulated and acquired data, showed an improvement in image spatial resolution with better behavior in the second case. In this way, small lesions, present on data, could be better discriminated in terms of contrast. Conclusions : Application of this approach to both simulated and acquired data suggests its efficacy in obtaining PET images of enhanced quality.

A Priori Information in Image Reconstruction

2003 ◽  
pp. 477-483 ◽  
Author(s):  
A. Yagola ◽  
B. Artamonov ◽  
V. Belokurov ◽  
E. Koptelova ◽  
M. Sazhin ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information

scholarly journals Removing Noise During the Filtering Images

2020 ◽  
Vol 9 (1) ◽  
pp. 2584-2587
Keyword(s):  
Image Processing ◽  
Statistical Models ◽  
Additive Noise ◽  
A Priori ◽  
Small Sample ◽  
Noise Filtering ◽  
Gaussian Filter ◽  
A Priori Information ◽  
Modified Kalman Filter ◽  
Priori Information

In the problems of image recognition, various approaches used when the image is noisy and there is a small sample of observations. The article discusses the issue of noise filtering in image processing. The lack of a priori information complicates the processing of data, as a result of which it is necessary to rely on some statistical models of signals and noise. The use of known filters does not always give the desired result. A Gaussian filter can be used for additive noise, a modified Kalman filter eliminates a wider range of noise

scholarly journals Microwave Radar Imaging of Heterogeneous Breast Tissue Integrating A Priori Information

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jochen Moll ◽  
Thomas N. Kelly ◽  
Dallan Byrne ◽  
Mantalena Sarafianou ◽  
Viktor Krozer ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Relative Permittivity ◽  
Breast Tissue ◽  
A Priori ◽  
A Priori Information ◽  
Image Reconstruction Techniques ◽  
Array Configuration ◽  
Microwave Radar ◽  
Priori Information ◽  
Dense Class

Conventional radar-based image reconstruction techniques fail when they are applied to heterogeneous breast tissue, since the underlying in-breast relative permittivity is unknown or assumed to be constant. This results in a systematic error during the process of image formation. A recent trend in microwave biomedical imaging is to extract the relative permittivity from the object under test to improve the image reconstruction quality and thereby to enhance the diagnostic assessment. In this paper, we present a novel radar-based methodology for microwave breast cancer detection in heterogeneous breast tissue integrating a 3D map of relative permittivity as a priori information. This leads to a novel image reconstruction formulation where the delay-and-sum focusing takes place in time rather than range domain. Results are shown for a heterogeneous dense (class-4) and a scattered fibroglandular (class-2) numerical breast phantom using Bristol’s 31-element array configuration.

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