Image Analysis Toolbox
Image Analysis Toolbox - Related Videos
08:27
Quantification of Oculomotor Responses and Accommodation Through Instrumentation and Analysis Toolboxes
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Cited by 2 •
2023
VisualEyes2020 (VE2020) is a custom scripting language that presents, records, and synchronizes visual eye movement stimuli. VE2020 provides stimuli for conjugate eye movements (saccades and smooth pursuit), disconjugate eye movements (vergence), accommodation, and combinations of each. Two analysis programs unify the data processing from the eye tracking and accommodation recording systems.
09:39
An Open-Source, Fully Customizable 5-Choice Serial Reaction Time Task Toolbox for Automated Behavioral Training of Rodents
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Cited by 1 •
2022
The present protocol describes the development of an open-source 5-choice serial reaction time task toolbox for rodent animal models, using Arduino and related hardware and a versatile Matlab toolbox, including an optional script for automated behavioral training. The scripts are customizable and facilitate the implementation of different trial-and-test designs.
05:41
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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Cited by 2 •
2020
The purpose of this protocol is to utilize pre-built convolutional neural nets to automate behavior tracking and perform detailed behavior analysis. Behavior tracking can be applied to any video data or sequences of images and is generalizable to track any user-defined object.
11:24
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
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Cited by 1 •
2025
The combination of iterative-bleaching-extends-multiplexity (IBEX) and a commercial nucleotide labeling assay (Click-iT EdU) enables the detection and categorization of dividing cell types in highly dynamic processes in fixed frozen murine tissue sections. Furthermore, a novel open-source image processing pipeline provides high-throughput image acquisition and analysis.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 35 •
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.
FAQs
Image enhancement improves the visibility of relevant structures, while filtering processes image information to help reduce unwanted variation or emphasize features of interest. These operations influence how clearly cells, tissues, or subcellular structures can be identified before measurement. Applying them consistently across experimental groups is important because processing choices can affect later segmentation and quantitative comparisons.
Segmentation separates structures of interest from the surrounding image, allowing the toolbox to treat cells, tissues, or subcellular components as measurable objects or regions. The quality of this separation directly affects feature extraction and subsequent measurements. If structures are not isolated consistently, estimates of size, shape, intensity, or spatial distribution may become less reliable across samples.
Once relevant regions are isolated, an Image Analysis Toolbox can extract measurements such as size, shape, intensity, and spatial distribution. These features describe different biological properties: size and shape characterize structure, intensity captures image signal, and spatial distribution describes how objects are positioned. Using several feature types can support more informative cell, tissue, or subcellular analyses.
Computational processing reduces reliance on subjective visual scoring by applying defined operations to image data. This supports more consistent measurement across samples and can make comparisons between experimental conditions easier to reproduce. The benefit is especially important for large image datasets, where manual assessment may be difficult to apply uniformly and may provide less directly quantitative evidence.
A typical workflow begins by processing the image with enhancement or filtering operations, followed by segmentation to isolate cells, tissues, or subcellular structures. The toolbox then extracts measurable features and supports interpretation of the resulting values. Researchers can use this sequence to convert visual observations into quantitative data suitable for assessing patterns and differences among experimental conditions.
Biological researchers can apply these tools to microscopy analysis, cell counting, phenotyping, and tissue characterization. They are useful when experiments generate visual evidence that must be measured rather than described only by observation. The same analytical approach can also help assess changes across experimental conditions, supporting systematic comparisons of cellular, tissue, or subcellular properties.
By measuring shared features across images, an Image Analysis Toolbox provides quantitative values for comparing experimental groups. Researchers may examine differences in structure size, shape, intensity, or spatial distribution, depending on the biological question. These measurements help connect visual changes with numerical evidence, strengthening interpretation of condition-dependent effects and making results easier to evaluate across image datasets.