Spatial Registration Assessments for the SNPP and N20 VIIRS Reflective Solar Bands Using Unscheduled Lunar Observations

2021 ◽  
pp. 1-12
Author(s):  
Truman Wilson ◽  
Xiaoxiong Xiong
Keyword(s):  
Spatial Registration ◽  
Lunar Observations

scholarly journals Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery

2021 ◽  
Vol 7 (10) ◽  
pp. 203
Author(s):  
Laura Connolly ◽  
Amoon Jamzad ◽  
Martin Kaufmann ◽  
Catriona E. Farquharson ◽  
Kevin Ren ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Deep Learning ◽  
Image Registration ◽  
Assessment Tool ◽  
Desorption Electrospray Ionization ◽  
Mass Spectrometry Data ◽  
Ionization Mass ◽  
Prostate Needle Biopsy ◽  
Imaging Tool ◽  
Spatial Registration

Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis.

ALEXIS Lunar Observations

1996 ◽  
pp. 465-470 ◽  
Author(s):  
B. C. Edwards ◽  
J. J. Bloch ◽  
D. Roussel-Dupré ◽  
T. E. Pfafman ◽  
Sean Ryan
Keyword(s):  
Lunar Observations

Relativistic Effects from Planetary and Lunar Observations of the XVIII–XX Centuries

1986 ◽  
pp. 315-328 ◽  
Author(s):  
G. A. Krasinsky ◽  
E. Yu. Aleshkina ◽  
E. V. Pitjeva ◽  
M. L. Sveshnikov
Keyword(s):  
Relativistic Effects ◽  
Lunar Observations

scholarly journals Report on the World Space Congress

1993 ◽  
Vol 156 ◽  
pp. 435-437
Author(s):  
Dennis D. Mccarthy
Keyword(s):  
Space Station ◽  
Washington Dc ◽  
Space Travel ◽  
Launch Vehicles ◽  
The World ◽  
Scientific Papers ◽  
Lunar Observations ◽  
Biological Aspects ◽  
Observational Techniques ◽  
International Astronautical

The World Space Congress comprised of the 43rd Congress of the International Astronautical Federation (IAF) and the 29th Plenary Meeting of the Committee of Space Research (COSPAR) was held in Washington, DC from 27 August to 4 September, 1992. Over 3000 people participated in the meetings where scientific papers were presented on such diverse topics as space travel, biological aspects of space travel, relativity, planetary atmospheres, space debris, space law, global change, launch vehicles, space station, space communication, navigation, Earth rotation, astrometry, satellite geodesy, use of lunar observations, and new observational techniques. Presentations dealing with the topics of this symposium are discussed, but complete reports will be forthcoming in the proceedings of the Congress.

scholarly journals ALEXIS Lunar Observations

1996 ◽  
Vol 152 ◽  
pp. 465-470
Author(s):  
B.C. Edwards ◽  
J.J. Bloch ◽  
D. Roussel-Dupré ◽  
T.E. Pfafman ◽  
Sean Ryan
Keyword(s):  
Solar Radiation ◽  
Extreme Ultraviolet ◽  
Solar Spectrum ◽  
Lunar Orbit ◽  
The Moon ◽  
Small Satellite ◽  
Large Area ◽  
Lunar Observations ◽  
Night Sky

The ALEXIS small satellite was designed as a large area monitor operating at extreme ultraviolet wavelengths (130 − 190 Å). At these energies, the moon is the brightest object in the night sky and was the first source identified in the ALEXIS data. Due to the design of ALEXIS and the lunar orbit, the moon is observed for two weeks of every month. Since lunar emissions in the extreme ultraviolet are primarily reflected solar radiation these observations may be useful as a solar monitor in the extreme ultraviolet. The data show distinct temporal and spectral variations indicating similar changes in the solar spectrum. We will present a preliminary dataset of lunar observations and discussions covering the variations observed and how they relate to the solar spectrum.

scholarly journals Mitigating the Impact of Field and Image Registration Errors through Spatial Aggregation

2019 ◽  
Vol 11 (3) ◽  
pp. 222 ◽  
Author(s):  
John Hogland ◽  
David L.R. Affleck
Keyword(s):  
Linear Models ◽  
Predictor Variable ◽  
Spatial Aggregation ◽  
Predictor Variables ◽  
Remotely Sensed Data ◽  
Substantial Impact ◽  
Landscape Characteristics ◽  
Registration Errors ◽  
Spatial Registration ◽  
The Impact

Remotely sensed data are commonly used as predictor variables in spatially explicit models depicting landscape characteristics of interest (response) across broad extents, at relatively fine resolution. To create these models, variables are spatially registered to a known coordinate system and used to link responses with predictor variable values. Inherently, this linking process introduces measurement error into the response and predictors, which in the latter case causes attenuation bias. Through simulations, our findings indicate that the spatial correlation of response and predictor variables and their corresponding spatial registration (co-registration) errors can have a substantial impact on the bias and accuracy of linear models. Additionally, in this study we evaluate spatial aggregation as a mechanism to minimize the impact of co-registration errors, assess the impact of subsampling within the extent of sample units, and provide a technique that can be used to both determine the extent of an observational unit needed to minimize the impact of co-registration and quantify the amount of error potentially introduced into predictive models.

scholarly journals Spatial registration of serial microscopic brain images to three-dimensional reference atlases with the QuickNII tool

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0216796 ◽  
Author(s):  
Maja A. Puchades ◽  
Gergely Csucs ◽  
Debora Ledergerber ◽  
Trygve B. Leergaard ◽  
Jan G. Bjaalie
Keyword(s):  
Three Dimensional ◽  
Brain Images ◽  
Spatial Registration
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