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Get Started with Medical Imaging

Medical imaging refers to the non-invasive acquisition and processing of images of the human body for clinical applications. Medical imaging often involves collaboration between clinicians, radiologists, and image processing experts. You can use medical image processing for applications including clinical diagnosis, surgical planning, and research.

This list provides some sample applications you can explore in Medical Imaging Toolbox™.

Medical Imaging Modalities

A medical imaging modality refers to the technology used to capture the image, such as X-ray, magnetic resonance imaging (MRI), or computed tomography (CT). Medical Imaging Toolbox supports most 2-D and 3-D modalities including, but not limited to, CT, MRI, positron emission tomography (PET), single-photon emission CT (SPECT), ultrasound, X-ray, X-ray angiography, mammography, and cine-MRI. You can also use Medical Imaging Toolbox to explore multimodal data sets, which combine multiple modalities. Additionally, the Medical Imaging Toolbox Interface for Cellpose Library support package enables cell segmentation in microscopy images.

Note

Medical Imaging Toolbox functionality generally works with all images in supported medical file formats, regardless of modality.

This table lists a subset of common imaging modalities. Each entry provides basic background information, a representative image, tips for getting started, and links to examples that use the corresponding modality.

ModalityImageGet StartedSee Also

Computed tomography (CT) — Visualize 3-D patient anatomy using X-rays.

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CT volume displayed as anatomical slice planes using the volshow function

For an example that uses this image [1], see Display Labeled Medical Image Volume in Patient Coordinate System.

Magnetic resonance imaging (MRI) — Visualize 3-D patient anatomy using a powerful magnetic field and radio waves.

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MRI volume displayed as anatomical slice planes using the volshow function

For an example that uses this image [2][3], see Brain MRI Segmentation Using Pretrained 3-D U-Net Network.

  • Interactively explore and label MRI volumes in a Medical Image Labeler 3-D volume session.

  • Programmatically load MRI volumes as a medicalVolume object.

  • Programmatically display MRI volumes using volshow, sliceViewer, or montage.

Positron emission tomography (PET) — Visualize metabolic or functional activity in 3-D using radioactive tracers.

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PET volume overlaid on CT volume in a slice planes volume display.

For an example that uses this image [4], see Display Multimodal Medical Image Data from PET and CT.

  • Interactively explore and label PET volumes in a Medical Image Labeler 3-D volume session.

  • Programmatically load CT volumes as a medicalVolume object.

  • Programmatically display PET volumes using volshow, sliceViewer, or montage.

  • Display multimodal PET/CT data by using the OverlayData argument of the volshow function.

Ultrasound — Visualize 2-D patient anatomy using sound waves.

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Ultrasound echocardiogram

For an example that uses this image, see Read, Process, and View Ultrasound Data.

  • Interactively explore and label ultrasound series in a Medical Image Labeler 2-D image session.

  • Programmatically load ultrasound series as a medicalImage object.

  • Programmatically display ultrasound series using implay or montage.

  • Extract and display individual frames by using extractFrame and imshow.

X-ray — Visualize 2-D anatomy using X-rays.

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Forearm X-ray

For an example that uses this image, see Create Medical Image Object from Filename.

  • Interactively explore and label X-ray images in a Medical Image Labeler 2-D image session.

  • Programmatically load X-ray images as a medicalImage object.

  • Programmatically display X-ray images using imshow.

Microscopy — Visualize tissue samples or cells at the microscopic level.

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Segmented microscopy image

For an example that uses this image, see Getting Started with Cellpose.

  • Load microscopy images using a function that supports your file type, such as imread or tiffreadVolume.

  • Segment cells using the cellpose object and its object functions.

Typical Workflow for Medical Image Analysis

Typical workflow for medical image analysis

Import and Spatial Referencing

A typical medical imaging workflow begins with importing data into the workspace. Medical images are available in file formats such as NIfTI, DICOM, NRRD, Analyze7.5, and Interfile. Medical Imaging Toolbox provides general 2-D and 3-D medical image objects, as well as file-format-specific functions, that you can use to import and export medical images. For more information, see Read, Process, and Write 3-D Medical Images.

Display, Volume Rendering, and Surfaces

Once you have imported a medical image into the workspace, you can view and inspect the image to plan your workflow. Medical Imaging Toolbox provides tools for 2-D and 3-D display, and for generating surfaces for 3-D printing or modeling applications. For more information, see Choose Approach for Medical Image Visualization.

Preprocessing and Augmentation

Image preprocessing and augmentation prepare images for advanced workflows. Use image preprocessing to reduce artifacts such as noise, or to register multiple images. For deep learning workflows, you can increase the size of your training data set using augmentation, which applies random transformations to simulate variations in image acquisition and patient anatomy. For more information, see Medical Image Preprocessing.

Ground Truth Labeling, Segmentation, and Analysis

The toolbox supports interactive and deep learning-based image segmentation and pixel labeling using the Medical Image Labeler app. Label large ground truth data sets for training deep learning networks, or segment individual images to isolate regions of interest for analysis workflows. You can analyze segmented images using standardized radiomics features, or develop custom analysis workflows using image processing functions. For more information, see Get Started with Medical Image Labeler and Segmentation and Analysis.

References

[1] Medical Segmentation Decathlon. "Lung." Tasks. Accessed May 10, 2018. http://medicaldecathlon.com/. The Medical Segmentation Decathlon data set is provided under the CC-BY-SA 4.0 license. All warranties and representations are disclaimed. See the license for details.

[2] “NITRC: CANDI Share: Schizophrenia Bulletin 2008: Tool/Resource Info.” Accessed October 17, 2022. https://www.nitrc.org/projects/cs_schizbull08/.

[3] Frazier, J. A., S. M. Hodge, J. L. Breeze, A. J. Giuliano, J. E. Terry, C. M. Moore, D. N. Kennedy, et al. “Diagnostic and Sex Effects on Limbic Volumes in Early-Onset Bipolar Disorder and Schizophrenia.” Schizophrenia Bulletin 34, no. 1 (October 27, 2007): 37–46. https://doi.org/10.1093/schbul/sbm120.

[4] Eslick, Enid M., John Kipritidis, Denis Gradinscak, Mark J. Stevens, Dale L. Bailey, Benjamin Harris, Jeremy T. Booth, and Paul J. Keall. “CT Ventilation as a Functional Imaging Modality for Lung Cancer Radiotherapy (CT-vs-PET-Ventilation-Imaging).” The Cancer Imaging Archive, 2022. https://doi.org/10.7937/3PPX-7S22.

See Also

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