Image Preprocessing
Image Preprocessing - Related Videos
08:49
Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
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Cited by 2 •
2022
This protocol describes methods for conducting magnetic resonance imaging, clearing, and immunolabeling of intact mouse brains using iDISCO+, followed by a detailed description of imaging using light-sheet microscopy, and downstream analyses using NuMorph.
06:51
PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
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Cited by 5 •
2025
This protocol presents PIPEMAT-RS, a standardized MATLAB-based preprocessing pipeline for resting-state EEG data. It ensures artifact removal, improves signal quality, and enhances data reproducibility across studies. The pipeline automates key preprocessing steps, including filtering, independent component analysis (ICA), and artifact classification, facilitating consistent and reliable EEG analysis for neurophysiological research.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 35 •
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.
09:08
Multiple-mouse Neuroanatomical Magnetic Resonance Imaging
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Cited by 56 •
2011
Magnetic resonance imaging (MRI) has become an increasingly popular tool for examining the phenotype of genetically altered mice. This article illustrates the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI.
06:46
Non-invasive In Vivo Fluorescence Optical Imaging of Inflammatory MMP Activity Using an Activatable Fluorescent Imaging Agent
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Cited by 4 •
2017
This paper explains the application of fluorescent imaging using an activatable optical imaging probe to visualize the in vivo activity of key matrix metalloproteinases in two different experimental models of inflammation.
FAQs
Uneven illumination can make identical structures appear different depending on their position in the image. Correcting this variation produces a more consistent intensity field, helping analysis distinguish true biological differences from imaging-related changes. This is particularly useful when comparing cells, tissues, or organelles across regions of a specimen or across multiple samples.
Noise reduction limits unwanted variation that can obscure cellular or tissue patterns, while contrast adjustment makes differences between image regions easier to interpret. These operations support the visibility of meaningful structures, but they should be selected carefully so that improvements in appearance do not interfere with structures needed for later segmentation, feature extraction, or classification.
Intensity normalization reduces variation in brightness or signal scale between images, whereas resizing and alignment make corresponding structures more comparable in size and position. Together, these operations help computational analyses evaluate samples under more consistent conditions. Their value is greatest when image measurements or automated decisions depend on comparing structures across specimens.
A workflow may begin by correcting uneven illumination and reducing noise, followed by contrast adjustment or intensity normalization when needed. Images can then be resized or aligned before analysis. The final preparation depends on the intended task, such as segmentation, feature extraction, or classification, and should retain the biological structures relevant to that task.
Researchers should match each operation to the variation that limits interpretation and to the downstream analysis. Illumination correction addresses spatial brightness differences, noise reduction improves visibility, and resizing or alignment supports comparisons. Because preprocessing can affect meaningful structures, the chosen sequence should prioritize consistent, interpretable images rather than applying every available adjustment.
Prepared images can support the detection and measurement of cells, tissues, organelles, and other biological patterns. They may also provide more suitable inputs for segmentation, feature extraction, and classification. By reducing variation introduced by imaging conditions, preprocessing can improve measurement reliability and help automated workflows produce more reproducible quantitative results.
Biological images may vary because of illumination, noise, intensity, size, or alignment rather than because the specimens differ biologically. Addressing these sources of variation makes image-derived measurements more consistent across samples. This strengthens quantitative studies by helping researchers distinguish specimen-related patterns from changes introduced during image acquisition or preparation.