Image Analysis Pipeline
Image Analysis Pipeline - Related Videos
09:41
A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
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Cited by 19 •
2016
We describe the steps to use our custom designed software for image integration, visualization and planning in epilepsy surgery.
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.
07:46
Optical Materialography Part 2: Image Analysis
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2023
Source: Faisal Alamgir, School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA The imaging of microscopic structures of solid materials, and the analysis of the structural components imaged, is known as materialography. Often, we would like to quantify the internal three-dimensional microstructure of a material using only the structural features evidenced by an exposed two-dimensional surface. While X-ray based tomographical methods can reveal buried...
09:10
Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline
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2025
Here, we present a protocol showcasing an optically transparent and flat substrate for the streamlined capture and analysis of contaminating particles in drinking water. Presented here is the Silicon Nanomembrane Analysis Pipeline (SNAP): a flexible pipeline for the capture, quantification, and identification of particles in liquid media.
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
Calibration makes image-based measurements more consistent by accounting for variation before analysis proceeds. It helps connect image values or features with meaningful, comparable observations rather than treating every difference as an environmental change. In monitoring programs, calibrated data support more reliable comparisons among locations, dates, or image collections and strengthen the reproducibility of ecological assessments.
Filtering reduces background variation or unwanted visual noise, making relevant patterns easier to detect. Segmentation then separates meaningful regions or features from the surrounding image. These operations serve different purposes: filtering improves the input for later analysis, while segmentation establishes the areas that can be measured, classified, or compared in environmental imagery.
Feature extraction converts selected image regions or patterns into measurable characteristics that can support interpretation. Those characteristics provide the basis for distinguishing categories during classification or calculating properties during quantification. In environmental studies, this step helps translate visual differences in vegetation, water features, habitat structure, or land cover into structured information suitable for analysis.
Results depend on how consistently images are acquired, calibrated, preprocessed, filtered, and segmented. Differences in background variation, image quality, spatial coverage, or timing can affect the patterns available for feature extraction and classification. Standardizing these stages reduces avoidable variation and makes measurements more comparable across environmental sites and through time.
A typical workflow starts with image acquisition, followed by preprocessing and calibration. Filtering can reduce background variation, after which segmentation identifies meaningful regions. Feature extraction supplies measurable characteristics, and classification or quantification produces the final interpretable results. Keeping these stages organized helps researchers trace how raw imagery becomes evidence for environmental assessment.
Researchers can apply image analysis when they need consistent measurements of land cover, vegetation condition, water features, habitat structure, or environmental change across time or space. The approach is especially useful for remote sensing and ecological assessment because it organizes image observations into comparable measurements that can inform monitoring and evidence-based management of natural systems.
The resulting measurements can reveal the distribution or condition of environmental features and support comparisons among places or time periods. Classification may distinguish meaningful patterns, while quantification expresses selected features as measurable results. These outputs provide structured evidence for ecological assessment, reproducible monitoring, remote sensing studies, and management decisions concerning natural systems.