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Related Experiment Video

Updated: Dec 30, 2025

Quantifying Intermembrane Distances with Serial Image Dilations
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Geometrical X-lets for Image Denoising.

Zahra Khodabandeh, Hossein Rabbani, Alireza Mehri

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study compares geometrical X-let transforms for image denoising. Steerable Pyramid excelled in synthetic OCT images for PSNR, while DT-CWT was best for SSIM. Real OCT images showed Curvelet for CNR and 2D-DWT for preservation.

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    Area of Science:

    • Image processing
    • Signal processing
    • Medical imaging

    Background:

    • Image denoising is crucial for enhancing image quality.
    • Nonlinear thresholding in time-frequency domains is a key denoising approach.
    • Geometrical X-let transforms offer diverse directional decompositions for image analysis.

    Purpose of the Study:

    • To comparatively evaluate various geometrical X-let transforms for image denoising.
    • To assess the performance of different transforms on both synthetic and real Optical Coherence Tomography (OCT) images.
    • To determine the optimal transform for specific denoising criteria and image types.

    Main Methods:

    • Comparative analysis of 2D-Discrete Wavelet (2D-DWT), Dual-Tree Complex Wavelet (DT-CWT), Curvelet, Contourlet, Steerable Pyramid (STP), and Circlet Transform (CT).
    • Application of nonlinear thresholding techniques within the transform domains.
    • Evaluation using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Contrast-to-Noise Ratio (CNR), Edge Preservation (EP), and Texture Preservation (TP).

    Main Results:

    • Steerable Pyramid (STP) demonstrated superior Peak Signal-to-Noise Ratio (PSNR) in synthetic OCT images.
    • Dual-Tree Complex Wavelet (DT-CWT) achieved the highest Structural Similarity Index (SSIM) for synthetic OCT images.
    • Curvelet Transform provided better Contrast-to-Noise Ratio (CNR) for real OCT images, while 2D-DWT excelled in Edge and Texture Preservation (EP/TP).

    Conclusions:

    • The effectiveness of geometrical X-let transforms for image denoising is dependent on the specific transform, image type, and evaluation criteria.
    • Steerable Pyramid and DT-CWT are highly effective for synthetic OCT image denoising based on PSNR and SSIM, respectively.
    • For real OCT images, Curvelet Transform and 2D-DWT offer advantages in CNR and preservation of image features, respectively.