Voxel Value Measurement
Voxel Value Measurement - Related Videos
07:12
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Cited by 23 •
2014
Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.
09:17
Measuring Grey Matter Differences with Voxel-based Morphometry: The Musical Brain
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Experience shapes the brain. It is well understood that our brains are different as a result of learning. While many experience-related changes manifest themselves at the microscopic level, for example by neurochemical adjustments in the behavior of individual neurons, we may also examine anatomical changes to the structure of the brain at a macroscopic level. One famous example of this kind of change...
12:08
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Cited by 14 •
2014
The bottleneck for cellular 3D electron microscopy is feature extraction (segmentation) in highly complex 3D density maps. We have developed a set of criteria, which provides guidance regarding which segmentation approach (manual, semi-automated, or automated) is best suited for different data types, thus providing a starting point for effective segmentation.
11:36
Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing
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Cited by 1 •
2022
This method demonstrates a voxel-based 3D printing workflow, which prints directly from medical images with exact spatial fidelity and spatial/contrast resolution. This enables the precise, graduated control of material distributions through morphologically complex, graduated materials correlated to radiodensity without loss or alteration of data.
06:26
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
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Cited by 100 •
2019
We detail how to conduct a meta-analysis of voxel-based neuroimaging studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI).
FAQs
The same numerical value can carry different meaning depending on the imaging system, the voxel’s coordinate position, and the calibration applied to the data. Calibration establishes how sampled measurements should be interpreted as intensity, density, or another property. Without this context, comparisons between locations or reconstructed objects may be misleading, reducing the reliability of engineering analysis.
A voxel value should be considered together with its position in the three-dimensional coordinate system. Location allows analysts to associate a measured property with a particular internal region, geometric feature, or suspected defect. This spatial relationship supports three-dimensional visualization and helps distinguish meaningful structural variation from an isolated numerical value without physical sectioning.
Voxel values may represent intensity, density, or another property measured through the sampling process. The selected interpretation depends on the imaging system and calibration rather than on the number alone. Recognizing what each scalar value represents allows engineers to use the data appropriately for material characterization, defect analysis, geometric inspection, or quantitative comparison.
Quantitative comparison uses the numerical values assigned throughout corresponding three-dimensional regions of reconstructed objects. When the data are interpreted consistently according to the imaging system and calibration, engineers can examine differences in internal structure, material-related properties, or geometry. This creates a measurement basis for evaluating reconstructed objects beyond visual inspection alone.
A typical workflow begins with sampled three-dimensional data, followed by interpretation of the stored values using the imaging system, coordinate position, and calibration. Analysts then visualize the volume and examine numerical patterns relevant to structure, materials, defects, or geometry. The resulting measurements can be organized for computational analysis, modeling, or quality-control evaluation.
This approach is useful when researchers need information about internal structures without physically sectioning the object. Voxel values can support material and defect characterization while preserving the reconstructed volume for three-dimensional visualization and further analysis. Engineering teams may apply the results to geometric inspection, quality control, model development, and computational evaluation of reconstructed objects.