Image Analysis Framework
Image Analysis Framework - Related Videos
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
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Cited by 1 •
2016
There is a critical need for tools and methodologies capable of managing aquatic systems in the face of uncertain future conditions. We provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework.
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.
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.
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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...
FAQs
Preprocessing prepares digital images for segmentation, the step that separates biological structures from surrounding image content. This creates a more consistent basis for identifying cells, tissues, or other objects before their properties are measured. In developmental biology, reliable separation helps reduce subjective interpretation when comparing cell shapes, tissue organization, or growth across microscopy datasets.
These steps answer different analytical questions. Segmentation identifies the structures or objects to analyze, feature extraction measures their properties, and classification distinguishes objects according to the information obtained from the image. Keeping these functions separate allows a framework to move from detecting biological structures to quantifying their characteristics and interpreting patterns in developmental samples.
Object tracking follows identified structures across sequential images, allowing researchers to monitor changes over time rather than examining each image independently. This temporal information supports analysis of cell migration, growth, and changing tissue organization. It also helps connect image measurements with developmental processes, making it possible to evaluate how patterns or structures evolve during embryonic and tissue development.
A structured framework applies the same sequence of computational steps, such as preprocessing, segmentation, feature extraction, classification, and tracking, to image datasets. Consistent processing reduces dependence on individual visual judgment and makes measurements easier to compare between experiments. This reproducibility supports quantitative models by providing more consistent observations of developmental structures and their changing properties.
A typical workflow begins by preparing the digital images, then segmenting relevant biological structures. The framework next extracts measurable features and may classify objects according to their image characteristics. When image sequences are available, tracking follows structures over time. The resulting measurements can then be interpreted to assess cell shape, tissue organization, growth, migration, or developmental patterning.
The framework can support quantitative measurements of cell shape, tissue organization, growth, migration, and developmental patterning. These outcomes arise by identifying structures, measuring their properties, distinguishing relevant objects, and monitoring changes across images. Such measurements give developmental biologists a way to compare tissue states and examine how biological organization changes during embryonic or tissue development.
It is especially useful when microscopy experiments produce large image datasets or when researchers need consistent comparisons across experiments. Computational processing reduces reliance on subjective interpretation while preserving information about biological structures and their properties. In developmental biology, this makes the approach valuable for analyzing complex changes in cells and tissues and for supporting quantitative interpretations of development.
Image-derived measurements provide quantitative observations that can be compared across developmental conditions or stages. By combining measurements of structure, organization, growth, migration, and patterning, researchers can examine relationships within developing embryos or tissues. These consistent data support quantitative models of development, linking visible changes in microscopy images with broader interpretations of how developmental organization changes over time.