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clahe algorithm  (MathWorks Inc)


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    Structured Review

    MathWorks Inc clahe algorithm
    Computational improvement of cancer sample recordings. ( a ) Image processing chain for cancer biopsies. The UM image stacks are contrast enhanced by contrast limited histogram equilibration <t>(CLAHE),</t> followed by stripe artifact removal using a matched 2D Fourier transform slope filter and unsharp masking. ( b1 ) Representative slice of an UM data set obtained from a breast cancer biopsy before post-processing. ( b2 ) Same as in ( b1 ) after contrast enhancement using CLAHE. The visibility of information encoded in small brightness differences is clearly enhanced. ( b3 ) Stripe artifacts generated during UM recording have been removed via a matched 2D Fourier domain slope filter. ( b4 ) Finally, the image is slightly sharpened via unsharp-masking to further enhance the visibility of fine details. ( c ) UM recordings often exhibit stripe shaped artefacts originating from light absorbing structures that are persistent to the clearing procedure. By obstructing the light sheet these structures produce visible shadows that can include an angle α with the horizontal image edges depending on the camera orientation. ( d ) To remove the stripe artefact the images are Fourier transformed and multiplied with a filter mask cutting out a pie-slice shaped piece of the spectrum matching the angular direction α of the stipes. After inverse transformation and rescaling a stripe suppressed image is obtained. ( e1 ) Design of the pie shaped filter. The angular direction α of the stripes corresponds to an angle of 90-α in the 2D power spectrum. The angular direction and the shape of the pie slice filter can be optimized in the software by varying α and the distances d 1 , d 2 , w 1 , and w 2 . This allows to match bandwidth and direction sensitivity of the filter in order to find a parameter combination providing best possible stripe suppression at minimal costs of blurring artefacts or ringing. ( e2 ) To reduce ringing artefacts due to a hard frequency cutoffs, the edges of the pie shaped filter exhibit a smooth Gaussian transition profile. All image processing steps were performed using custom-made software written <t>in</t> <t>MATLAB</t> (MathWorks, Germany) and Visual Basic.Net (Microsoft, USA). The programs can be obtained from klaus.becker@twien.ac.at upon reasonable request.
    Clahe Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2932 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/clahe+algorithm+in/Image+Processing+Toolbox/pmc07572501-213-7-12
    Average 96 stars, based on 2932 article reviews
    clahe algorithm - by Bioz Stars, 2026-10
    96/100 stars

    Images

    1) Product Images from "3D histopathology of human tumours by fast clearing and ultramicroscopy"

    Article Title: 3D histopathology of human tumours by fast clearing and ultramicroscopy

    Journal: Scientific Reports

    doi: 10.1038/s41598-020-71737-w

    Computational improvement of cancer sample recordings. ( a ) Image processing chain for cancer biopsies. The UM image stacks are contrast enhanced by contrast limited histogram equilibration (CLAHE), followed by stripe artifact removal using a matched 2D Fourier transform slope filter and unsharp masking. ( b1 ) Representative slice of an UM data set obtained from a breast cancer biopsy before post-processing. ( b2 ) Same as in ( b1 ) after contrast enhancement using CLAHE. The visibility of information encoded in small brightness differences is clearly enhanced. ( b3 ) Stripe artifacts generated during UM recording have been removed via a matched 2D Fourier domain slope filter. ( b4 ) Finally, the image is slightly sharpened via unsharp-masking to further enhance the visibility of fine details. ( c ) UM recordings often exhibit stripe shaped artefacts originating from light absorbing structures that are persistent to the clearing procedure. By obstructing the light sheet these structures produce visible shadows that can include an angle α with the horizontal image edges depending on the camera orientation. ( d ) To remove the stripe artefact the images are Fourier transformed and multiplied with a filter mask cutting out a pie-slice shaped piece of the spectrum matching the angular direction α of the stipes. After inverse transformation and rescaling a stripe suppressed image is obtained. ( e1 ) Design of the pie shaped filter. The angular direction α of the stripes corresponds to an angle of 90-α in the 2D power spectrum. The angular direction and the shape of the pie slice filter can be optimized in the software by varying α and the distances d 1 , d 2 , w 1 , and w 2 . This allows to match bandwidth and direction sensitivity of the filter in order to find a parameter combination providing best possible stripe suppression at minimal costs of blurring artefacts or ringing. ( e2 ) To reduce ringing artefacts due to a hard frequency cutoffs, the edges of the pie shaped filter exhibit a smooth Gaussian transition profile. All image processing steps were performed using custom-made software written in MATLAB (MathWorks, Germany) and Visual Basic.Net (Microsoft, USA). The programs can be obtained from klaus.becker@twien.ac.at upon reasonable request.
    Figure Legend Snippet: Computational improvement of cancer sample recordings. ( a ) Image processing chain for cancer biopsies. The UM image stacks are contrast enhanced by contrast limited histogram equilibration (CLAHE), followed by stripe artifact removal using a matched 2D Fourier transform slope filter and unsharp masking. ( b1 ) Representative slice of an UM data set obtained from a breast cancer biopsy before post-processing. ( b2 ) Same as in ( b1 ) after contrast enhancement using CLAHE. The visibility of information encoded in small brightness differences is clearly enhanced. ( b3 ) Stripe artifacts generated during UM recording have been removed via a matched 2D Fourier domain slope filter. ( b4 ) Finally, the image is slightly sharpened via unsharp-masking to further enhance the visibility of fine details. ( c ) UM recordings often exhibit stripe shaped artefacts originating from light absorbing structures that are persistent to the clearing procedure. By obstructing the light sheet these structures produce visible shadows that can include an angle α with the horizontal image edges depending on the camera orientation. ( d ) To remove the stripe artefact the images are Fourier transformed and multiplied with a filter mask cutting out a pie-slice shaped piece of the spectrum matching the angular direction α of the stipes. After inverse transformation and rescaling a stripe suppressed image is obtained. ( e1 ) Design of the pie shaped filter. The angular direction α of the stripes corresponds to an angle of 90-α in the 2D power spectrum. The angular direction and the shape of the pie slice filter can be optimized in the software by varying α and the distances d 1 , d 2 , w 1 , and w 2 . This allows to match bandwidth and direction sensitivity of the filter in order to find a parameter combination providing best possible stripe suppression at minimal costs of blurring artefacts or ringing. ( e2 ) To reduce ringing artefacts due to a hard frequency cutoffs, the edges of the pie shaped filter exhibit a smooth Gaussian transition profile. All image processing steps were performed using custom-made software written in MATLAB (MathWorks, Germany) and Visual Basic.Net (Microsoft, USA). The programs can be obtained from klaus.becker@twien.ac.at upon reasonable request.

    Techniques Used: Generated, Transformation Assay, Software

    Related Articles

    Fluorescence:

    Article Title: Multimodal Mapping of Cranial Sutures Guides Application of an MMP13-Inhibiting Hydrogel Patch to Restore Asymmetric Mineralization and Prevent Craniosynostosis.
    Article Snippet: .. Highresolution fluorescence images for Ca, P, O, Si, N, and C were acquired, followed by intensity quantification and normalization in MATLAB (Image Processing Toolbox). ..

    Article Title: CaMKII monomers are sufficient for GluN2B binding, co-condensation, and synaptic potentiation.
    Article Snippet: .. Time-lapse imaging and analysis of CaMKII puncta fusion and fission Time-lapse fluorescence movies were acquired as single-channel (CaMKII) 2D TIFF stacks at ∼15.7 s intervals and analyzed in MATLAB (MathWorks) using the Image Processing Toolbox. .. The overall particle detection and linking strategy was conceptually adapted from the Fiji TrackMate framework (Tinevez et al.68 and Ershov et al.69) but implemented here in a custom MATLAB pipeline.

    Imaging:

    Article Title: Hepatic HIF2α Modulates Extra‐Hepatic Disease‐Associated Phenotypes During Metabolic Dysfunction‐Associated Steatotic Liver Disease
    Article Snippet: Images were captured at ×20 magnification with a VS200 Research Slide Scanner (Olympus) using Slideview 5.0, imported to QuPath v0.5.1 and converted into RGB files. .. ImageJ (v.1.45 g) was used to perform background correction before importing the images into MATLAB (MATLAB and Imaging Processing Toolbox 2024a, MathWorks). .. The SMASH toolbox (GitHub—SMASHtoolbox/Release: Version 1.0 of the SMASH Toolbox, n.d.) was used to perform segmentation and fiber type analysis in MATLAB.

    Article Title: CaMKII monomers are sufficient for GluN2B binding, co-condensation, and synaptic potentiation.
    Article Snippet: .. Time-lapse imaging and analysis of CaMKII puncta fusion and fission Time-lapse fluorescence movies were acquired as single-channel (CaMKII) 2D TIFF stacks at ∼15.7 s intervals and analyzed in MATLAB (MathWorks) using the Image Processing Toolbox. .. The overall particle detection and linking strategy was conceptually adapted from the Fiji TrackMate framework (Tinevez et al.68 and Ershov et al.69) but implemented here in a custom MATLAB pipeline.

    Automated:

    Article Title: Impact of tranexamic acid on visualization in full endoscopic spine surgery: a randomized controlled trial
    Article Snippet: .. Automated Video-Based Analysis All surgical videos were reviewed using an automated image-processing pipeline developed in MATLAB (version R2026a). ..

    Analysis:

    Article Title: Impact of tranexamic acid on visualization in full endoscopic spine surgery: a randomized controlled trial
    Article Snippet: .. Automated Video-Based Analysis All surgical videos were reviewed using an automated image-processing pipeline developed in MATLAB (version R2026a). ..

    Article Title: CaMKII monomers are sufficient for GluN2B binding, co-condensation, and synaptic potentiation.
    Article Snippet: .. Time-lapse imaging and analysis of CaMKII puncta fusion and fission Time-lapse fluorescence movies were acquired as single-channel (CaMKII) 2D TIFF stacks at ∼15.7 s intervals and analyzed in MATLAB (MathWorks) using the Image Processing Toolbox. .. The overall particle detection and linking strategy was conceptually adapted from the Fiji TrackMate framework (Tinevez et al.68 and Ershov et al.69) but implemented here in a custom MATLAB pipeline.

    Magnetic Resonance Imaging:

    Article Title: Preoperative Prediction of Ductal Carcinoma In Situ Upstaging Using Machine Learning-Based Peritumoral Breast MRI Radiomics.
    Article Snippet: Background: A subset of biopsyconfirmed ductal carcinoma in situ (DCIS) cases can be upstaged to invasive breast cancer at surgery.. Preoperative identification of upstaging risk is essential for treatment planning.. Purpose: To develop clinical and breast MRI radiomics models to preoperatively predict upstaging in DCIS.

    Sequencing:

    Article Title: Preoperative Prediction of Ductal Carcinoma In Situ Upstaging Using Machine Learning-Based Peritumoral Breast MRI Radiomics.
    Article Snippet: Background: A subset of biopsyconfirmed ductal carcinoma in situ (DCIS) cases can be upstaged to invasive breast cancer at surgery.. Preoperative identification of upstaging risk is essential for treatment planning.. Purpose: To develop clinical and breast MRI radiomics models to preoperatively predict upstaging in DCIS.



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    Image Search Results


    Data extracted from included studies.

    Journal: Biology

    Article Title: Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

    doi: 10.3390/biology11101412

    Figure Lengend Snippet: Data extracted from included studies.

    Article Snippet: Paduano et al., Italy, 2013. [ ] , Developing an analysis pipeline which can automatically process images of stem cell colonies in optical microscopy in order to study markers of embryonic stem cells (ESCs) heterogeneity , Mouse ESCs , 57 images , A proposed approach in MATLAB, CLAHE (image adjustment) Orientation matching algorithm , The tested algorithm achieved proper image processing..

    Techniques: Labeling, High Content Screening, Biomarker Discovery, Generated, Extraction, Plasmid Preparation, Microscopy, Cell Culture, Modification, Cell Differentiation, Transformation Assay, Software, Confocal Microscopy, Construct, Fluorescence