scholarly journals Solid State Imaging Techniques. Pseudo-color Reduction by Signal Processing for Single-Imaging Device Color Cameras with Primary Filter.

1999 ◽  
Vol 53 (2) ◽  
pp. 302-309
Author(s):  
Naoki Ozawa
Keyword(s):  
Signal Processing ◽  
Solid State ◽  
Imaging Techniques ◽  
Imaging Device ◽  
Color Reduction ◽  
Pseudo Color ◽  
Color Cameras

Scanning Laser Ophthalmoscopy

2017 ◽  
pp. 18-21
Keyword(s):  
Light Intensity ◽  
Laser Beam ◽  
Imaging Technique ◽  
Morphological Changes ◽  
Imaging Techniques ◽  
Tissue Level ◽  
Scanning Laser ◽  
Scanning Laser Ophthalmoscopy ◽  
Imaging Device ◽  
Scanning Laser Microscopy

The aim of retinal imaging techniques is visualization of morphological changes at the cellular and tissue level. Various techniques are used for this purpose. The scanning laser ophthalmoscopy (SLO), a retinal optical imaging device based on standard scanning laser microscopy is an imaging technique that scans the fundus with a highly collimated narrow laser beam and measures the backscattered light intensity. Here, progress on developing SLO instruments and their applications in ophthalmology are reviewed.

A Versatile High-Vacuum Cryo-Transfer for Cryo-FESEM, Cryo-SPM and other Imaging Techniques

1999 ◽  
Vol 5 (S2) ◽  
pp. 424-425
Author(s):  
Martin Ritter ◽  
Didier Henry ◽  
Stefan Wiesner ◽  
Stephan Pfeiffer ◽  
Roger Wepf
Keyword(s):  
Structural Integrity ◽  
Imaging Techniques ◽  
High Vacuum ◽  
Specimen Preparation ◽  
Ice Crystal ◽  
Structural Water ◽  
Imaging Device ◽  
The Past ◽  
Cold Stage ◽  
Immobilization Techniques

A structure preservation of biological and organic samples, close to native state, can only be reached by cryo immobilization techniques. Cryo immobilization allows not only to preserve the high structural integrity but also to arrest dynamic processes in the μs- ms range.After freezing the sample and preparing the surface of interest, it is important to prevent the sample from ice crystal damage, removal of structural water, condensation of water or other contaminants until imaging. Therefore, ideally the samples are kept below the recrystallisation temperature of water (< 147K) during the transfer from the preparation environment into the imaging chamber.For the transfer of frozen samples several concepts have been followed in the,past: a) the specimen after manipulation/preparation were submersed in liquid nitrogen and transferred to the cold stage of the microscope or b) a preparation chamber was permanently attached to the microscope column allowing the direct transfers between the preparation chamber and the cold stage in the microscope. These concepts allow either a high grade of flexibility combined with a high risk of contamination or to prevent contamination but combined with inflexibility. In addition the later also does not allow using the microscope during the specimen preparation procedure, nor transferring the specimen to an other imaging device.

scholarly journals Solid State Imaging Techniques. A 1/5-inch 270,000 pixels FT-CCD Color Image Sensor.

1995 ◽  
Vol 49 (2) ◽  
pp. 176-181 ◽  
Author(s):  
Yoshihiro Okada ◽  
Tohru Watanabe ◽  
Tatsuya Ishiguro ◽  
Kazuo Takeda
Keyword(s):  
Solid State ◽  
Color Image ◽  
Imaging Techniques ◽  
Image Sensor

scholarly journals Gradients of connectivity as graph fourier bases of brain activity

2021 ◽  
pp. 1-15
Author(s):  
Giulia Lioi ◽  
Vincent Gripon ◽  
Abdelbasset Brahim ◽  
François Rousseau ◽  
Nicolas Farrugia
Keyword(s):  
Signal Processing ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Complex Structure ◽  
Brain Networks ◽  
Graph Signal Processing ◽  
As Graph ◽  
Functional Relevance ◽  
And Function

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity “coupled to” an underlying network in graph Fourier modes). These studies have used a variety of imaging techniques (e.g., fMRI, electroencephalography, diffusion-weighted and myelin-sensitive imaging) and connectivity estimators to model brain networks. Results are promising in terms of interpretability and functional relevance, but methodologies and terminology are variable. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.

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