Scanned Image Analysis
Scanned Image Analysis - Related Videos
07:19
Clock Scan Protocol for Image Analysis: ImageJ Plugins
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Cited by 16 •
2017
This paper describes two novel ImageJ plugins for 'Clock Scan' image analysis. These plugins expand the functionality of the original visual basic 6 program and, most importantly, make the program available to a large research community by bundling it with the ImageJ free image analysis software package.
10:25
Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy
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Cited by 15 •
2011
The full process from brain specimen preparation to serial sectioning imaging using the Knife-Edge Scanning Microscope, to data visualization and analysis is described. This technique is currently used to acquire mouse brain data, but it is applicable to other organs, other species.
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.
07:47
Analysis of Brain Mitochondria Using Serial Block-Face Scanning Electron Microscopy
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Cited by 32 •
2016
Mitochondrial visualization and analysis from mammalian brain tissue is a challenging task. Here, we describe how three dimensional (3D) reconstruction analysis from the serial block-face scanning electron microscopy (SBFSEM) can be used to gain insights on the morphological and volumetric analysis of this critical energy generating organelle.
03:25
Imaging of Neuronal Mitochondria Using a Serial Block-Face Scanning Electron Microscope
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2025
Source: Mukherjee, K., et. al. Analysis of Brain Mitochondria Using Serial Block-Face Scanning Electron Microscopy. J. Vis. Exp. (2016)This video demonstrates the preparation, imaging, and analysis of mouse brain tissue using a serial block-face scanning electron microscope (SBFSEM) to visualize mitochondrial ultrastructure, enabling precise mapping of their morphology and spatial distribution in axonal and dendritic neuronal compartments.
FAQs
Segmentation separates regions of interest from the surrounding background, allowing software to analyze selected structures rather than the entire image indiscriminately. After this separation, researchers can quantify features such as cell number, size, morphology, staining intensity, or spatial distribution. In microscopy and histology, accurate segmentation therefore converts visible biological patterns into measurements that can be compared across samples.
Feature extraction summarizes measurable characteristics of image regions, such as their dimensions, shape, intensity, or location. Classification then uses those characteristics to distinguish among regions, objects, or visual patterns. Keeping these functions conceptually separate helps researchers understand whether an analysis is measuring biological properties, assigning categories, or performing both tasks within the same workflow.
Preprocessing and segmentation determine which image information enters the quantitative analysis. If regions of interest are not distinguished consistently from background, measurements of cell number, morphology, intensity, or distribution may not be comparable between samples. Applying a consistent computational workflow helps improve reproducibility and makes it easier to relate observed image differences to experimental or disease-related changes.
A typical workflow begins with a digitized image, followed by preprocessing to prepare the image for computational examination. The analysis then identifies regions of interest through segmentation, extracts measurable features from those regions, and classifies structures or patterns when needed. The resulting measurements can be organized for comparison among biological samples, experimental conditions, or observed disease-related changes.
The approach can be applied to microscopy images, histological sections, gel documentation, and scanned biological specimens. The specific output depends on the material being examined: analyses may count cells, compare cell size or morphology, measure staining intensity, or assess how structures are distributed spatially. This range makes the method useful across cellular, tissue, and specimen-level investigations.
It links visual observations with quantitative biological outcomes, enabling researchers to compare samples using recorded measurements rather than descriptions alone. In disease studies, the analysis can help assess changes in structures or staining patterns; in experiments, it can evaluate differences among conditions. Automated examination also supports large-scale analysis and can improve reproducibility when many images require consistent assessment.