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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Related Experiment Video

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Automated Segmentation of Head Computed Tomography Images Using FSL.

Keith A Cauley, Joe Och, Patrick J Yorks

    Journal of Computer Assisted Tomography
    |August 9, 2017
    PubMed
    Summary

    The FSL FAST tool effectively segments head CT scans for brain analysis. This automated method improves gray and white matter quantitation and is enhanced by bias field correction for better results.

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    Area of Science:

    • Neuroimaging
    • Medical Image Analysis
    • Radiology

    Background:

    • Automated brain image segmentation is crucial for quantitative analysis.
    • Existing methods may have limitations in accuracy and completeness.

    Purpose of the Study:

    • To evaluate the FSL (FMRIB Software Library) tool, specifically FAST, for automated segmentation of head CT images.
    • To assess the impact of bias field correction on image segmentation quality.

    Main Methods:

    • Head CT images were processed using FSL's BET for brain extraction and FAST for segmentation.
    • Segmentation outputs were analyzed using histograms.
    • The effect of intensity inhomogeneity correction was tested with simulated and real CT data.

    Main Results:

    • FSL FAST directly segmented head CT images, enabling quantification of gray and white matter.
    • Segmentation with FAST was more complete than masking techniques.
    • Bias field correction significantly reduced image signal intensity variance (P < 0.01), improving segmentation accuracy.

    Conclusions:

    • FSL FAST provides a robust method for direct automated segmentation of head CT images.
    • Bias field correction enhances the reliability of FSL-based CT brain segmentation.