Medical Image Analysis
Medical Image Analysis - Related Videos
08:21
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
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Cited by 4 •
2014
Optical detection of ultrasound is impractical in many imaging scenarios because it often requires stable environmental conditions. We demonstrate an optical technique for ultrasound sensing in volatile environments with miniaturization and sensitivity levels appropriate for optoacoustic imaging in restrictive scenarios, e.g. intravascular applications.
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Cited by 3 •
2014
Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.
11:24
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
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Cited by 1 •
2025
The combination of iterative-bleaching-extends-multiplexity (IBEX) and a commercial nucleotide labeling assay (Click-iT EdU) enables the detection and categorization of dividing cell types in highly dynamic processes in fixed frozen murine tissue sections. Furthermore, a novel open-source image processing pipeline provides high-throughput image acquisition and analysis.
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.
09:36
Safety Precautions and Operating Procedures in an (A)BSL-4 Laboratory: 4. Medical Imaging Procedures
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Cited by 8 •
2016
Here, we present an overview of the preparation and animal handling procedures required to safely perform medical imaging in an animal biosafety level 4 laboratory. Computed tomography of a mock-infected guinea pig illustrates these procedures that may be used to evaluate the disease caused by a high consequence pathogen.
FAQs
Each processing stage contributes a different type of information. Image enhancement prepares visual data, segmentation separates structures or regions for analysis, feature extraction produces measurable characteristics, registration supports use of image data together, and classification assigns analyzed patterns to categories. Combining these operations turns raw modality data into measurements and visualizations that can support diagnosis, treatment planning, or research.
Machine learning can automate or assist image-analysis tasks rather than replacing the entire workflow. It may be applied to operations such as segmentation, feature extraction, or classification, while the broader pipeline still depends on imaging data and engineered processing steps. This flexibility lets engineers design systems that improve consistency and scale analysis across biomedical investigations.
Medical image analysis must accommodate data from X-ray, computed tomography, magnetic resonance imaging, and ultrasound. Because the workflow is described across these modalities, engineers can apply common computational stages while producing modality-specific image data for analysis. The choice of imaging source therefore forms part of the engineering context, even though the goal remains extracting useful information about the body.
An engineering workflow typically moves from image enhancement toward segmentation, feature extraction, registration, and classification, with machine learning assisting selected stages. The sequence is not merely a list of algorithms: each step contributes to the final measurements or visualizations. Those outputs can then be used to identify abnormalities, quantify anatomy, or monitor disease progression.
It is useful when clinicians or researchers need more than a visual image. Computed measurements can support treatment planning, track disease progression, and guide interventions, while analyzed images also contribute to biomedical research. These uses make the technique relevant to both clinical workflows and engineering studies of anatomy and disease.
Engineering contributes imaging science, signal processing, and artificial intelligence to a workflow that produces reproducible measurements and visualizations. These capabilities help transform medical images into data that can be quantified and used for more personalized healthcare. The subject connection is practical: engineering supplies computational methods for turning complex image information into usable evidence.