Image Analysis Program
Image Analysis Program - Related Videos
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.
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.
07:44
Evaluation of Changes in Hydration and Body Cell Mass with Bioelectrical Impedance Analysis after Exercise Program for Rheumatoid Arthritis Patients
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Cited by 1 •
2023
This protocol assesses alterations in hydration and body cell mass status using bioelectrical impedance vectorial analysis following a dynamic exercise program designed for patients with rheumatoid arthritis. The dynamic exercise program itself is detailed, highlighting its components focused on cardiovascular capacity, strength, and coordination. The protocol details steps, instruments, and limitations.
07:29
Programmed Electrical Stimulation in Mice
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Cited by 50 •
2010
Programmed electrical stimulation provides the ability to determine conduction properties of the heart, and the possibility to induce and terminate cardiac arrhythmias using various pacing protocols. Using a transvenous catheter, intracardiac electrogram recordings can be obtained in mice following programmed electrical stimulation protocols to identify arrhythmogenic substrates.
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.
FAQs
Calibration and enhancement serve different purposes in an Image Analysis Program. Calibration supports consistent measurement from image pixels, while enhancement improves the visibility of visual patterns before analysis. Keeping these stages distinct helps researchers distinguish clearer imagery from actual environmental differences and supports more reliable comparisons among images.
Segmentation separates an image into objects or regions that can be measured independently. This step allows the program to distinguish areas such as vegetation, water, land cover, or habitat features before calculating their properties. Accurate separation improves the relevance of later measurements because color, shape, size, and texture can be assigned to meaningful regions.
Classification assigns image objects or regions to categories according to measurable properties such as color, shape, size, or texture. These features provide the basis for organizing visual information into interpretable groups. In environmental analysis, classification can help turn patterns in field photographs or remote-sensing images into data suitable for comparison and evaluation.
A typical workflow begins with a digital image and proceeds through calibration, enhancement, segmentation, feature measurement, and classification. Researchers first prepare the image for consistent analysis, then isolate relevant regions and quantify their properties. The resulting measurements can be organized for spatial comparison, monitoring, or assessment of environmental change.
Researchers can use an Image Analysis Program when visual evidence must be converted into consistent measurements. Environmental applications described for these tools include assessing land cover, vegetation condition, water clarity, habitat structure, and changes visible in field photographs or remote-sensing images. The approach is especially useful when repeated observations or spatial comparisons are required.
Image analysis can provide quantitative information about the color, shape, size, and texture of objects or regions in an image. Depending on the environmental target, those measurements can describe vegetation condition, habitat structure, water clarity, or land-cover patterns. Comparing results across images helps researchers evaluate spatial differences and observed ecosystem change.
By reducing reliance on manual scoring and applying consistent image-processing steps, the method supports repeated environmental observations. Researchers can compare measurements from field photographs or remote-sensing images to identify changes in land cover, vegetation, water, or habitat structure. These comparisons contribute to evidence-based evaluation of ecosystem change and environmental conditions.