Low contrast detectability in CT for human and model observers in multi-slice data sets

2015 ◽  
Author(s):  
Alexandre Ba ◽  
Damien Racine ◽  
Julien G. Ott ◽  
Francis R. Verdun ◽  
Sabine Kobbe-Schmidt ◽  
...  
2014 ◽  
Vol 30 ◽  
pp. e18-e19
Author(s):  
I. Hernandez-Giron ◽  
A. Calzado ◽  
J. Geleijns ◽  
R.M.S. Joemai ◽  
W.J.H. Veldkamp
Keyword(s):  

2015 ◽  
Vol 31 (7) ◽  
pp. 798-807 ◽  
Author(s):  
I. Hernandez-Giron ◽  
A. Calzado ◽  
J. Geleijns ◽  
R.M.S. Joemai ◽  
W.J.H. Veldkamp
Keyword(s):  

1998 ◽  
Vol 52 (6) ◽  
pp. 790-796 ◽  
Author(s):  
Jeremy J. Andrew ◽  
Mark A. Browne ◽  
Ian E. Clark ◽  
Tom M. Hancewicz ◽  
Allen J. Millichope

The use of Raman microscopy in imaging two emulsion systems is described. Registered optical microscopy and Raman images are collected, the latter describing the chemical basis of the heterogeneity observed in the former. These examples act as a powerful demonstration of the application of the Raman microscopy technique to the analysis and understanding of microstructure in commercial products. The results indicate how the principles of Raman imaging can be applied to complex, multicomponent, multiphase systems of inherently low contrast. Such systems are of importance because they represent a wide variety of commercial product systems, ranging from pharmaceutical creams through skin creams and toothpastes. The use of a software environment for the organization, storage, management, interrogation, and manipulation of multidimensional spectral imaging data is also described. The important factors to be considered in determining the full information content of such data sets are established, and suggestions as to how such data sets can be optimally interrogated are made.


2018 ◽  
Vol 52 ◽  
pp. 61
Author(s):  
Luca Bellesi ◽  
Rolf Wyttenbach ◽  
Diego Gaudino ◽  
Maria Antonietta Piliero ◽  
Francesco Pupillo ◽  
...  

2019 ◽  
Vol 64 ◽  
pp. 89-97 ◽  
Author(s):  
Raffaele Villa ◽  
Nicoletta Paruccini ◽  
Antonia Baglivi ◽  
Michele Signoriello ◽  
Roberto Alejandro Montezuma Velasquez ◽  
...  
Keyword(s):  

2021 ◽  
pp. 1-10
Author(s):  
Lei Chen ◽  
Jun Han ◽  
Feng Tian

Fusing the infrared (IR) and visible images has many advantages and can be applied to applications such as target detection and recognition. Colors can give more accurate and distinct features, but the low resolution and low contrast of fused images make this a challenge task. In this paper, we proposed a method based on parallel generative adversarial networks (GANs) to address the challenge. We used IR image, visible image and fusion image as ground truth of ‘L’, ‘a’ and ‘b’ of the Lab model. Through the parallel GANs, we can gain the Lab data which can be converted to RGB image. We adopt TNO and RoadScene data sets to verify our method, and compare with five objective evaluation parameters obtained by other three methods based on deep learning (DL). It is demonstrated that the proposed approach is able to achieve better performance against state-of-arts methods.


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