Image Analysis Algorithms
Image Analysis Algorithms - Related Videos
07:05
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
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2022
We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.
12:39
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Cited by 6 •
2012
Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.
12:29
Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
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Cited by 11 •
2017
Two image analysis algorithms, "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics" were created, to automatically quantify nine morphological features of the Drosophila neuromuscular junction (NMJ).
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 34 •
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
Preprocessing reduces image noise before the algorithm measures features, making boundaries, intensities, and patterns easier to evaluate consistently. This step supports more reliable segmentation, which separates regions of interest from the surrounding image. In chemical imaging and microscopy, improved separation can help distinguish particles, structures, or assay regions that might otherwise produce inconsistent measurements.
Segmentation first identifies the regions or objects that should be analyzed. Feature extraction then converts those selected regions into measurable properties, such as pixel intensity, shape, texture, or spatial relationships. Together, these stages transform a visual image into structured information that can be compared across samples, supporting classification and analysis of chemical or material features.
Classification uses extracted image features to organize or distinguish visual patterns. Intensity, shape, texture, and spatial relationships can provide different bases for separating regions or objects within an image set. In chemistry, this supports interpretation when samples show visually different particles, materials, or assay responses, allowing computational results to supplement observations made by eye.
A typical workflow begins with image preprocessing to reduce noise, followed by segmentation of relevant regions. The selected regions are then measured through feature extraction, using properties such as intensity, shape, texture, or spatial arrangement. Classification may organize the resulting patterns, while comparisons across many images reveal changes or recurring features linked to chemical samples or reactions.
In chemistry, these algorithms support microscopy, particle characterization, material analysis, and interpretation of visual assay results. Their value differs by application: microscopy can provide measurements of visible structures, particle studies can quantify object properties, and material analysis can compare visual features. Assay analysis can also connect image-based changes with chemical behavior across samples.
Automated measurement allows the same image features to be evaluated across large datasets rather than relying only on individual visual inspection. This can improve reproducibility and accelerate analysis while revealing patterns or changes that are difficult to recognize by eye. The resulting measurements help connect visual observations with chemical composition, structure, and reaction behavior.