Imaging System
Imaging System - Related Videos
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Cited by 3 •
2014
Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.
10:06
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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Cited by 8 •
2012
This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 34 •
2015
We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.
16:44
Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging
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Cited by 15 •
2009
We suggest a Born normalized approach for Optical Projection Tomography (BnOPT) that accounts for the absorption properties of imaged samples to obtain accurate and quantitative fluorescence tomographic reconstructions. We use the proposed algorithm to reconstruct the fluorescence molecular probe distribution within small animal organs.
09:08
Multiple-mouse Neuroanatomical Magnetic Resonance Imaging
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Cited by 59 •
2011
Magnetic resonance imaging (MRI) has become an increasingly popular tool for examining the phenotype of genetically altered mice. This article illustrates the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI.
FAQs
These performance properties control different aspects of biological observation. Spatial resolution determines how finely structures can be distinguished, while contrast affects separation between features. Sensitivity influences how reliably the system detects light or another emitted signal, and acquisition speed determines how effectively changes can be captured. Together, they shape the accuracy of morphological, localization, and dynamic measurements.
After optics focus light or an emitted signal, the detector records it as image data. Those measurements provide the input for image-processing software, which transforms recorded data into measurable features. This linkage matters because observations of biological structures or changes depend on both signal capture and subsequent conversion into analyzable information.
The interaction generates or modifies the signal that carries information about the specimen. The resulting light or emitted signal can then be collected, focused, and recorded for analysis. Its quality influences whether researchers can identify structures, determine where components are located, follow changes over time, or evaluate responses to experimental conditions.
A typical workflow begins by directing illumination or another signal toward the specimen. Lenses or related optics collect and focus the resulting signal onto a detector, which records the data. Image-processing software then converts those data into measurable features. Researchers can use the processed output to examine structure, location, change, or experimental response.
Researchers should relate system performance to the feature they need to measure. Spatial resolution is important when distinguishing fine structures, whereas contrast and sensitivity affect visibility and detection. Acquisition speed becomes important for observing changes. Evaluating these properties against the intended biological observation helps determine whether recorded images can support reliable measurement.
In biology, imaging systems support examination of cells, tissues, and organisms across several types of questions. Images can reveal morphology, show where features are localized, track dynamic changes, and document responses to experimental conditions. Processing the recorded data into measurable features allows these observations to move beyond visual inspection toward structured scientific analysis.