scholarly journals A Bayesian segmentation methodology for parametric image models

1995 ◽  
Vol 17 (2) ◽  
pp. 211-217 ◽  
Author(s):  
S.M. LaValle ◽  
S.A. Hutchinson
Keyword(s):  
Parametric Image ◽  
Bayesian Segmentation ◽  
Image Models

scholarly journals Adapting the ITK Registration Framework to Fit Parametric Image Models

2010 ◽  
Author(s):  
Cory Quammen ◽  
Russell M. Taylor II
Keyword(s):  
Image Registration ◽  
Gaussian Function ◽  
Input Image ◽  
Parametric Models ◽  
Parametric Image ◽  
Image Models

The image registration framework in the Insight Tookit offers a powerful body of code for finding the optimal spatial transform that registers one image with another. However, ITK currently lacks a way to fit parametric models of image pixel values to an input image. This document describes new classes that enable use of the registration framework to provide this capability. We describe a new base class, itk::ParametricImageSource, that defines an interface for parametric image sources. An adapter class itk::ImageToParametricImageSourceMetric that enables itk::ParametricImageSources to be hooked into the registration framework is also described. An example adapter class that enables the existing itk::GaussianImageSource to be used for image fitting is presented, and we demonstrate use of the classes by fitting a 2D Gaussian function to an image generated by the itk::GaussianImageSource class.

scholarly journals Resolved spectral variations of the centimetre-wavelength continuum from the ρ Oph W photo-dissociation-region

2021 ◽  
Author(s):  
Simon Casassus ◽  
Matías Vidal ◽  
Carla Arce-Tord ◽  
Clive Dickinson ◽  
Glenn J White ◽  
...  
Keyword(s):  
Molecular Cloud ◽  
Dust Emission ◽  
Image Synthesis ◽  
Microwave Emission ◽  
Radio Continuum ◽  
Continuum Emission ◽  
Parametric Image ◽  
Physical Conditions ◽  
Morphological Differences ◽  
Dust Grain

Abstract Cm-wavelength radio continuum emission in excess of free-free, synchrotron and Rayleigh-Jeans dust emission (excess microwave emission, EME), and often called ‘anomalous microwave emission’, is bright in molecular cloud regions exposed to UV radiation, i.e. in photo-dissociation regions (PDRs). The EME correlates with IR dust emission on degree angular scales. Resolved observations of well-studied PDRs are needed to compare the spectral variations of the cm-continuum with tracers of physical conditions and of the dust grain population. The EME is particularly bright in the regions of the ρ Ophiuchi molecular cloud (ρ Oph) that surround the earliest type star in the complex, HD 147889, where the peak signal stems from the filament known as the ρ Oph-W PDR. Here we report on ATCA observations of ρ Oph-W that resolve the width of the filament. We recover extended emission using a variant of non-parametric image synthesis performed in the sky plane. The multi-frequency 17 GHz to 39 GHz mosaics reveal spectral variations in the cm-wavelength continuum. At ∼30 arcsec resolutions, the 17-20 GHz intensities follow tightly the mid-IR, Icm∝I(8 μm), despite the breakdown of this correlation on larger scales. However, while the 33-39 GHz filament is parallel to IRAC 8 μm, it is offset by 15–20 arcsec towards the UV source. Such morphological differences in frequency reflect spectral variations, which we quantify spectroscopically as a sharp and steepening high-frequency cutoff, interpreted in terms of the spinning dust emission mechanism as a minimum grain size acutoff ∼ 6 ± 1 Å that increases deeper into the PDR.

scholarly journals Improved inference in Bayesian segmentation using Monte Carlo sampling: Application to hippocampal subfield volumetry

2013 ◽  
Vol 17 (7) ◽  
pp. 766-778 ◽  
Author(s):  
Juan Eugenio Iglesias ◽  
Mert Rory Sabuncu ◽  
Koen Van Leemput
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling ◽  
Bayesian Segmentation

Mapping cancer signaling networks by an integrated multiplexed tissue imaging platform

TECHNOLOGY ◽  
2016 ◽  
Vol 04 (03) ◽  
pp. 174-193 ◽  
Author(s):  
Esteban Roberts ◽  
Thomas Jacob ◽  
Karl Garsha ◽  
Damien Ramunno-Johnson ◽  
Franklin Ventura ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Pi3k Pathway ◽  
Tissue Imaging ◽  
Signaling Networks ◽  
Signaling Proteins ◽  
Parametric Image ◽  
Fluorescent Reporters ◽  
Specimen Collection ◽  
Therapeutic Development ◽  
Subcellular Resolution

Aberrant cellular signaling networks are implicated in major diseases including cancer, but are difficult to reliably quantitate, as many signaling proteins are expressed at low abundance and further reduced following specimen collection. MTIP is an integrated tissue-imaging platform that leverages bright, fluorescent reporters and sensitive spectral instrumentation, along with automated staining, image acquisition and non-parametric image analysis, to attain reproducible, multiplexed quantitation of signaling proteins in tissue. MTIP captured the phosphoactivity of six key PI3K/MAPK proteins (pAKTS473, pAKT308, pPRAS40, pS6, peIF4G and pERK1/2) at high precision (coefficient of variation, CV <10%), four-log dynamic range and subcellular resolution. We demonstrated the MTIP platform's capability to capture a diversity of PI3K/MAPK networks present in breast tumors. These protein networks are heterogeneously distributed across the tumor tissue and associated with subgroups of cells and underscore the importance of accessing information about signaling networks in spatially intact tissue. Analysis of PI3K/MAPK networks by hierarchical clustering showed that PI3K/MAPK networks do not strictly correlate with PI3K pathway mutations, also pointing to the value of functional signaling network profiling together with genomic information. MTIP's reliable quantitative capability can be applied to guide therapeutic development and selection in precision medicine.

scholarly journals Modelling and forecasting of quasi-periodic processes in technical objects based on cylindrical image models

2019 ◽  
pp. 387-393
Author(s):  
V R Krasheninnikov ◽  
Yu E Kuvayskova
Keyword(s):  
Time Series ◽  
Technical Condition ◽  
The State ◽  
Effective Management ◽  
Hydraulic Unit ◽  
Forecasting Models ◽  
Periodic Processes ◽  
Image Models ◽  
Accuracy Of Prediction ◽  
Modelling And Forecasting

Accurate forecasting of the state of technical objects is necessary for effective management. The technical condition of the object is characterized by a system of time series of monitored indicators. The time series often have difficultly predictable irregular periodicity (quasi-periodicity). In this paper, to improve the accuracy of such series forecasting, models of quasi-periodic processes in the form of samples of a cylindrical image are used. The application of these models is demonstrated by forecasting of a hydraulic unit vibrations. It is shown that the use of these models provides a higher accuracy of prediction compared with the classical approaches.

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