Image Processing Workflow
Image Processing Workflow - Related Videos
13:43
A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion
0 Views •
Cited by 17 •
2022
This article describes how to effectively utilize three cryo-EM processing platforms, i.e., cryoSPARC v3, RELION-3, and Scipion 3, to create a single and robust workflow applicable to a variety of single-particle data sets for high-resolution structure determination.
09:03
Human Mesenchymal Stem Cell Processing for Clinical Applications Using a Closed Semi-Automated Workflow
0 Views •
Cited by 3 •
2023
Here, we present a protocol to harvest adherent cells from multi-layered flasks in a closed semi-automated manner using a counterflow centrifugation system. This protocol can be applied for harvesting both adherent and suspension cells from other cell expansion platforms with few modifications to the existing steps.
13:54
A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
0 Views •
Cited by 14 •
2023
This protocol provides an approach to formulation optimization over mixture, continuous, and categorical study factors that minimizes subjective choices in the experimental design construction. For the analysis phase, an effective and easy-to-use modeling fitting procedure is employed.
11:24
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
0 Views •
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.
10:09
EasyFiji: A Graphical Interface for User-Friendly Fluorescence Image Processing in Fiji
0 Views •
2026
EasyFiji is a graphical user interface plugin for Fiji (ImageJ) that provides a curated suite of fluorescence image visualization and processing tools frequently utilized by life scientists.
FAQs
Preprocessing improves interpretability by reducing noise and correcting background variation before measurements are made. Noise can obscure genuine visual patterns, while uneven background can make regions appear different for reasons unrelated to the biochemical sample. Applying these corrections creates a more consistent basis for comparing image features across samples and helps later measurements better reflect experimental differences.
Segmentation determines which pixels or regions belong to the feature being studied. Its quality directly affects calculated intensity, area, and shape, because including irrelevant background or excluding part of a region changes those values. In biochemical images, a carefully defined region of interest helps distinguish the signal associated with a molecular pattern from surrounding image content.
Intensity, area, and shape describe different properties of a detected region. Intensity can help relate visual strength to molecular abundance or experimental response, area can quantify the extent of a signal, and shape can describe its spatial pattern. Considering these features together provides a more informative comparison than relying on a visual impression alone.
Validation against controls tests whether measured image features support a reliable interpretation rather than reflecting processing or background effects. Controls provide a reference for judging observed patterns and experimental responses, helping researchers evaluate consistency across samples. This step is important when visual measurements are used to connect image data with molecular abundance, localization, or biochemical change.
An effective workflow preserves the same logic across acquisition, preprocessing, segmentation, measurement, and validation. Raw images are first obtained, then cleaned and corrected, followed by selection of relevant regions and extraction of features. The resulting measurements should be checked against controls before samples are compared, so each stage contributes to a reproducible interpretation.
The approach supports analysis of microscopy images, protein gels, immunoblots, and other experimental readouts. In microscopy, processing can help examine localization or visual response; in gels and immunoblots, measured image features can support comparisons related to molecular abundance. The same structured logic makes visual evidence more suitable for quantitative biochemical analysis.
Consistent processing reduces the chance that differences between samples arise only from how images were handled. Using comparable preprocessing, region selection, and feature measurement makes intensity, area, or shape values easier to compare, while controls help assess whether observed patterns reflect experimental response. This supports reproducibility and strengthens conclusions drawn from visual biochemical data.