Matlab Imaging
Matlab Imaging - Related Videos
01:24
Introduction to MATLAB
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2025
MATLAB stands for Matrix Laboratory. MathWorks developed MATLAB as a multi-paradigm numerical computing environment and proprietary programming language. It has evolved significantly over the years to become a tool utilized by engineers, scientists, and mathematicians for various tasks, including matrix calculations, developing algorithms, data analysis, and visualization. MATLAB's applications span various industries and disciplines. It's used in image and signal processing, communications,...
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...
06:51
PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
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Cited by 3 •
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.
05:28
Clinical Imaging of Microwave Mammography
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2025
This protocol describes the imaging procedure of a microwave imaging device for breast imaging.
09:52
Non-invasive Optical Imaging of the Lymphatic Vasculature of a Mouse
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Cited by 25 •
2013
Recently developed imaging techniques using near-infrared fluorescence (NIRF) may help elucidate the role the lymphatic system plays in cancer metastasis, immune response, wound repair, and other lymphatic-associated diseases.
FAQs
Because digital images are represented as numerical arrays, MATLAB imaging can apply mathematical operations to image data rather than treating each image as an unstructured picture. Filtering, contrast enhancement, registration, segmentation, and feature extraction modify or interpret those values in defined ways. This numerical representation supports quantitative measurements and makes image-processing workflows easier to script, repeat, and evaluate.
These operations answer different analytical questions. Registration aligns images so that corresponding anatomical regions or changes can be compared, segmentation separates structures or regions of interest, and feature extraction derives measurable characteristics from those regions. Using them in sequence can transform complex medical images into organized information for identifying anatomy, measuring tissue characteristics, or tracking changes over time.
Interactive workflows allow users to inspect images and apply processing operations directly, while scripted workflows document the sequence of analysis steps in a reproducible form. The two approaches can complement each other: visual exploration helps guide interpretation, and scripting helps apply a defined method consistently across imaging data. This combination is valuable when developing biomedical image-analysis methods.
A typical workflow begins with acquiring or importing digital image data, followed by processing operations selected for the analytical goal. The user may enhance contrast, filter data, register related images, segment structures, or extract features, then visualize and interpret the results. Organizing these stages around a specific question helps convert raw imaging data into measurable findings.
Researchers can use MATLAB imaging when medical or biomedical images require more than visual inspection. The approach supports analysis of radiographs, magnetic resonance images, computed tomography scans, microscopy, and other clinical or research data. Depending on the dataset and objective, users may identify anatomical structures, quantify tissue characteristics, or develop an analysis method suited to a particular imaging study.
Its value comes from converting image information into interpretable and quantifiable results. Analysis can help identify anatomical structures, measure tissue characteristics, and track changes across time, providing information relevant to diagnosis and treatment planning. In biomedical research, the same capabilities support development of reproducible image-analysis methods and evaluation of imaging data across clinical or experimental investigations.