Computational Image Analysis
Computational Image Analysis - Related Videos
09:42
Immunostaining for DNA Modifications: Computational Analysis of Confocal Images
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Cited by 8 •
2017
Newly discovered oxidized forms of 5-methylcytosine (oxi-mCs), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) may represent distinct DNA modifications with unique functional roles. Here a semi-quantitative workflow for visualization of oxi-mCs' spatial distribution, signal intensity profiling and colocalization is described.
09:57
How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
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Cited by 168 •
2012
Measuring gyrification (cortical folding) at any age represents a window into early brain development. Hence, we previously developed an algorithm to measure local gyrification at thousands of points over the hemisphere1. In this paper, we detail the computation of this local gyrification index.
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.
02:05
Visualization of a Cerebral Thrombus in a Mouse Using Micro-Computed Tomography Imaging
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2025
Source: Kim, D., et. al. Combined Near-infrared Fluorescent Imaging and Micro-computed Tomography for Directly Visualizing Cerebral Thromboemboli. J. Vis. Exp. (2016)This video demonstrates visualization of cerebral thrombus in mice using micro-computed tomography (micro-CT) imaging. A cerebral thrombus mouse model is injected with fibrin-targeted gold nanoparticles acting as a contrasting agent. The mouse is placed on the bed of a micro-CT machine, and images are acquired from multiple angles.
09:36
A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
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Cited by 1 •
2025
Assessment of internal carotid artery (ICA) stenosis is based on the estimation of percentage stenosis, which does not account for physiologically relevant risk factors for stroke such as plaque composition and hemodynamics. This protocol leverages quantitative magnetic resonance imaging and computational fluid dynamics to characterize ICA plaque composition and hemodynamics.
FAQs
Preprocessing prepares digital images before later analysis. Its purpose is to condition the image so segmentation, feature extraction, and measurement operate on a usable representation rather than on unprocessed visual observations. In biological studies, this stage helps support reproducible quantitative comparisons across image datasets and experiments.
Segmentation identifies the cells or structures that will be analyzed as separate objects or regions. The quality of this step directly affects later measurements because object boundaries determine estimates of size and shape, while selected regions influence intensity and spatial analysis. Accurate object identification therefore provides the foundation for meaningful biological comparisons.
Feature extraction converts image content into measurable descriptors such as size, shape, intensity, and spatial organization. Classification then uses processed image information to distinguish or organize objects or patterns. Together, these operations connect numerical image properties with biological categories, supporting studies of cell morphology, tissue architecture, and disease-associated changes.
Computational image analysis reduces subjectivity by replacing purely visual judgments with defined algorithmic operations and quantitative outputs. When the same processing logic is applied across images, researchers can compare measured size, shape, intensity, or spatial organization rather than relying only on personal assessment. This is especially relevant when datasets are large and consistent evaluation is needed.
Images from microscopy and other imaging systems can serve as inputs when they are available in digital form for computational processing. The imaging source determines what visual information enters the pipeline, while subsequent analysis can quantify cells, structures, or broader tissue patterns. This connects instrument-generated observations with measurable biological data.
Applications span several biological scales and questions. At the cellular level, analysis can characterize morphology; at the tissue level, it can examine architecture and spatial organization. The same approach can also support investigation of disease-associated changes and dynamic biological processes, allowing researchers to translate complex image patterns into measurements for comparison.