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gray-level co-occurrence matrix (glcm) algorithm  (MathWorks Inc)


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    Structured Review

    MathWorks Inc gray-level co-occurrence matrix (glcm) algorithm
    Flowchart of the radiomics analysis framework. (A) The QSM, R2*, and T1-weighted images employed, and the features extracted from them. (B) The general linear model constructed from 121 normal controls to control for the influences of age and sex on the extracted original brain features. (C) The final informative radiomics features truncated through a data-driven feature selection. (D) The random forest framework used in the machine-learning training-testing cycles, which was parallelly tested on the patients with PD with different clinical statuses. Of note, independent external validation was conducted using an untouched database (database-106). EPD: Early PD; <t>GLCM:</t> Gray-Level Co-Occurrence Matrix; M-LPD: Moderate-to-late PD; NC: Normal controls; PD: Parkinson’s disease; PD-nonTD: non-tremor-dominant PD; PD-TD: Tremor-dominant PD; QSM: Quantitative susceptibility mapping.
    Gray Level Co Occurrence Matrix (Glcm) Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/gray-level co-occurrence matrix (glcm) algorithm/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    gray-level co-occurrence matrix (glcm) algorithm - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson’s disease: a brain radiomics study"

    Article Title: A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson’s disease: a brain radiomics study

    Journal: Neural Regeneration Research

    doi: 10.4103/1673-5374.339493

    Flowchart of the radiomics analysis framework. (A) The QSM, R2*, and T1-weighted images employed, and the features extracted from them. (B) The general linear model constructed from 121 normal controls to control for the influences of age and sex on the extracted original brain features. (C) The final informative radiomics features truncated through a data-driven feature selection. (D) The random forest framework used in the machine-learning training-testing cycles, which was parallelly tested on the patients with PD with different clinical statuses. Of note, independent external validation was conducted using an untouched database (database-106). EPD: Early PD; GLCM: Gray-Level Co-Occurrence Matrix; M-LPD: Moderate-to-late PD; NC: Normal controls; PD: Parkinson’s disease; PD-nonTD: non-tremor-dominant PD; PD-TD: Tremor-dominant PD; QSM: Quantitative susceptibility mapping.
    Figure Legend Snippet: Flowchart of the radiomics analysis framework. (A) The QSM, R2*, and T1-weighted images employed, and the features extracted from them. (B) The general linear model constructed from 121 normal controls to control for the influences of age and sex on the extracted original brain features. (C) The final informative radiomics features truncated through a data-driven feature selection. (D) The random forest framework used in the machine-learning training-testing cycles, which was parallelly tested on the patients with PD with different clinical statuses. Of note, independent external validation was conducted using an untouched database (database-106). EPD: Early PD; GLCM: Gray-Level Co-Occurrence Matrix; M-LPD: Moderate-to-late PD; NC: Normal controls; PD: Parkinson’s disease; PD-nonTD: non-tremor-dominant PD; PD-TD: Tremor-dominant PD; QSM: Quantitative susceptibility mapping.

    Techniques Used: Construct, Control, Selection, Biomarker Discovery



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    gray-level co-occurrence matrix (glcm) algorithm  (MathWorks Inc)
    90
    MathWorks Inc gray-level co-occurrence matrix (glcm) algorithm
    Flowchart of the radiomics analysis framework. (A) The QSM, R2*, and T1-weighted images employed, and the features extracted from them. (B) The general linear model constructed from 121 normal controls to control for the influences of age and sex on the extracted original brain features. (C) The final informative radiomics features truncated through a data-driven feature selection. (D) The random forest framework used in the machine-learning training-testing cycles, which was parallelly tested on the patients with PD with different clinical statuses. Of note, independent external validation was conducted using an untouched database (database-106). EPD: Early PD; <t>GLCM:</t> Gray-Level Co-Occurrence Matrix; M-LPD: Moderate-to-late PD; NC: Normal controls; PD: Parkinson’s disease; PD-nonTD: non-tremor-dominant PD; PD-TD: Tremor-dominant PD; QSM: Quantitative susceptibility mapping.
    Gray Level Co Occurrence Matrix (Glcm) Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/gray-level co-occurrence matrix (glcm) algorithm/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    gray-level co-occurrence matrix (glcm) algorithm - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Flowchart of the radiomics analysis framework. (A) The QSM, R2*, and T1-weighted images employed, and the features extracted from them. (B) The general linear model constructed from 121 normal controls to control for the influences of age and sex on the extracted original brain features. (C) The final informative radiomics features truncated through a data-driven feature selection. (D) The random forest framework used in the machine-learning training-testing cycles, which was parallelly tested on the patients with PD with different clinical statuses. Of note, independent external validation was conducted using an untouched database (database-106). EPD: Early PD; GLCM: Gray-Level Co-Occurrence Matrix; M-LPD: Moderate-to-late PD; NC: Normal controls; PD: Parkinson’s disease; PD-nonTD: non-tremor-dominant PD; PD-TD: Tremor-dominant PD; QSM: Quantitative susceptibility mapping.

    Journal: Neural Regeneration Research

    Article Title: A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson’s disease: a brain radiomics study

    doi: 10.4103/1673-5374.339493

    Figure Lengend Snippet: Flowchart of the radiomics analysis framework. (A) The QSM, R2*, and T1-weighted images employed, and the features extracted from them. (B) The general linear model constructed from 121 normal controls to control for the influences of age and sex on the extracted original brain features. (C) The final informative radiomics features truncated through a data-driven feature selection. (D) The random forest framework used in the machine-learning training-testing cycles, which was parallelly tested on the patients with PD with different clinical statuses. Of note, independent external validation was conducted using an untouched database (database-106). EPD: Early PD; GLCM: Gray-Level Co-Occurrence Matrix; M-LPD: Moderate-to-late PD; NC: Normal controls; PD: Parkinson’s disease; PD-nonTD: non-tremor-dominant PD; PD-TD: Tremor-dominant PD; QSM: Quantitative susceptibility mapping.

    Article Snippet: Second, three dimensional texture features were measured using the Gray-Level Co-Occurrence Matrix (GLCM) algorithm (Haralick et al., 1973) written in Matlab 2018a ( https://ww2.mathworks.cn/products/matlab.html ).

    Techniques: Construct, Control, Selection, Biomarker Discovery