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A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger

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    A novel Region Of Interest (ROI) characterization balances dark and bright areas in Magnetic Resonance (MR) images for effective noise removal evaluation. This method optimizes noise reduction while preserving crucial image details in medical imaging.

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    Area of Science:

    • Medical Imaging
    • Image Processing
    • Signal Analysis

    Background:

    • Evaluating image denoising performance is challenging, especially when noise-free ground truth is unavailable.
    • Magnetic Resonance (MR) imaging is susceptible to noise, impacting diagnostic accuracy.
    • Existing methods often struggle to balance noise removal with the preservation of essential image features.

    Purpose of the Study:

    • To introduce a new Region Of Interest (ROI) characterization technique for evaluating image denoising performance in MR images.
    • To develop a method that effectively balances noise reduction and image detail preservation.
    • To validate the proposed ROI technique using both synthetic and real medical imaging data.

    Main Methods:

    • A novel ROI characterization method is proposed, focusing on contrast balancing between dark and bright regions.
    • The technique tracks noise removal by analyzing changes in ROI contrast.
    • The Semi-Classical Signal Analysis (SCSA) method, utilizing Schrödinger operator eigenfunctions and soft thresholding, is employed for denoising and evaluation.

    Main Results:

    • The proposed ROI characterization effectively evaluates denoising performance by balancing noise removal and detail preservation.
    • Testing on synthetic MRI data from the BrainWeb database demonstrated the technique's efficacy.
    • Application to the SCSA denoising method showed promising results on real MRI data.

    Conclusions:

    • The developed ROI characterization is a suitable tool for evaluating denoising performance in medical image processing, particularly when ground truth images are absent.
    • This technique offers an optimal compromise between noise suppression and the retention of critical image information.
    • The method facilitates reliable assessment of advanced denoising algorithms in practical medical applications.