Image Registration Approach
Image Registration Approach - Related Videos
07:13
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Cited by 1 •
2023
The combination of multiple imaging modalities is often necessary to gain a comprehensive understanding of pathophysiology. This approach utilizes phantoms to generate a differential transformation between the coordinate systems of two modalities, which is then applied for co-registration. This method eliminates the need for fiducials in production scans.
01:27
Shelf Registration
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2026
Shelf registration is an essential tool for companies to raise capital flexibly and efficiently. It is a provision by the Securities and Exchange Commission (SEC) that enables companies to file a single registration for securities they intend to issue over time rather than all at once. This process offers multiple advantages.Flexibility and Convenience: Shelf registration allows companies to issue securities in phases per market conditions and funding needs rather than raising all capital at...
09:43
In vivo Near Infrared Fluorescence (NIRF) Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis
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Cited by 32 •
2011
We detail a new near-infrared fluorescence (NIRF) catheter for 2-dimensional intravascular molecular imaging of plaque biology in vivo. The NIRF catheter can visualize key biological processes such as inflammation by reporting on the presence of plaque-avid activatable and targeted NIR fluorochromes. The catheter utilizes clinical engineering and power requirements and is targeted for application in human coronary arteries. The following research study describes a multimodal imaging strategy...
10:25
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Cited by 30 •
2019
Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.
08:24
Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
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2016
This protocol describes a method to quantify mouse sociability. Mice are videotaped as they move and interact in a special cage. Movie processing allows for the automated quantification of sociability with excellent accuracy and reliability.
FAQs
The transformation category should match the discrepancy between images. Rigid adjustments address positional differences such as translation and rotation, affine adjustments can also accommodate scale differences, and nonrigid adjustments address changes in shape. Choosing appropriately is important because the transformation determines how accurately corresponding biological structures are aligned for later comparison.
Landmarks provide explicit spatial features, whereas intensity patterns use image-value distributions to identify corresponding regions. Registration can use either source or combine both, allowing the alignment to draw on recognizable structures and broader image content. This flexibility is useful when comparing microscopy images, experimental conditions, or complementary imaging modalities.
These differences represent distinct ways that corresponding features can occupy different positions or configurations across images. A transformation that corrects position or orientation may not address scale or shape changes. Accounting for the relevant difference helps prevent residual misalignment, which otherwise can weaken visualization and reduce the reliability of downstream biological comparisons.
A typical workflow identifies corresponding information through landmarks, intensity patterns, or both, estimates a spatial transformation, and applies that transformation to the images. The selected adjustment may be rigid, affine, or nonrigid, depending on whether the data differ in translation, rotation, scale, or shape. The resulting alignment supports subsequent visualization and analysis.
Registration is useful when researchers need to compare the same biological specimen across time points while accounting for changes in image position, orientation, scale, or shape. Once aligned, the images can support analysis of cell movement and tissue changes. This makes temporal comparisons more spatially consistent and helps reveal biological change rather than imaging-related displacement.
Images from complementary modalities can be aligned so that measurements from one modality correspond spatially with structures or signals observed in another. Using landmarks, intensity patterns, or both provides a basis for estimating the necessary transformation. The aligned datasets can then be combined to construct more accurate spatial maps and support integrated biological interpretation.
In biology, accurate alignment supports studies of structure, development, disease progression, and treatment response. It can also help quantify tissue changes and track cell movement across images or experimental conditions. By placing corresponding features in matching positions, registration strengthens visualization and provides a more reliable foundation for comparing biological states.