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Related Concept Videos

Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence
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Shading correction for whole slide image using low rank and sparse decomposition.

Tingying Peng, Lichao Wang, Christine Bayer

    Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    |October 22, 2014
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    Summary

    This study introduces novel algorithms to correct shading artifacts in whole slide images (WSI). These methods effectively remove illumination variations, improving image quality for digital pathology and microscopy analysis.

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    Area of Science:

    • Microscopy and Digital Pathology
    • Image Processing and Computer Vision

    Background:

    • Microscopic imaging often faces intensity inhomogeneity, known as shading artifacts, caused by uneven illumination or camera issues.
    • These artifacts, particularly visible as seams in whole slide images (WSI), hinder subsequent analyses like segmentation and registration.

    Purpose of the Study:

    • To develop and validate two new retrospective shading correction algorithms for whole slide images (WSI).
    • To address common WSI formats: pre-mosaicked image tiles and already-stitched images.

    Main Methods:

    • The proposed methods utilize matrix rank minimization and sparse signal recovery techniques.
    • Shading correction is reformulated as a low-rank and sparse component decomposition problem.
    • A sparse constraint in the Fourier domain ensures background illumination smoothness.

    Main Results:

    • Extensive validation on synthetic and real microscopy images demonstrates superior shading removal performance.
    • The algorithms effectively separate foreground objects from background illumination fields.
    • Comparison with a well-established ImageJ method shows improved results.

    Conclusions:

    • The developed algorithms offer effective retrospective shading correction for WSI.
    • These methods enhance image quality for critical downstream applications in digital pathology and microscopy.
    • The approach provides a robust solution for a common challenge in microscopic imaging.