gray-level co-occurrence matrix (glcm) algorithm (MathWorks Inc)
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gray-level co-occurrence matrix (glcm) algorithm
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/product/gray-level+co-occurrence+matrix+(glcm)+algorithm/pmc09165377-119-9-20
Average 90 stars, based on 1 article reviews
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/product/gray-level+co-occurrence+matrix+(glcm)+algorithm/pmc09165377-119-9-20
Average 90 stars, based on 1 article reviews
gray-level co-occurrence matrix (glcm) algorithm - by Bioz Stars,
2026-10
90/100 stars
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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
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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Selection:Article Title: Texture analysis using Horadam polynomial coefficient estimate for the class of Sakaguchi kind function Article Snippet: Figure 2 Representation of Article Title: A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson’s disease: a brain radiomics study Article Snippet: Second, three dimensional texture features were measured using the Article Title: Classification of Artocarpus species based on leaf recognition using multiclass support vector machine Article Snippet: Leaf extraction is done by using Gray Level Co-occurrence Matrix (GLCM) analysis in [4] that are performed by using Article Title: Texture-based brain networks for characterization of healthy subjects from MRI Article Snippet: Texture parameters from each ROI were then extracted from the images, using the gray level co-occurrence matrix (GLCM) approach , implemented through homemade Article Title: Texture analysis using Horadam polynomial coefficient estimate for the class of Sakaguchi kind function. Article Snippet: Representation of Article Title: Investigation of structural and physico-chemical properties of rice starch with varied amylose content: A combined microscopy, spectroscopy, and thermal study Article Snippet: Starch from a given botanical source can vary considerably in terms of physicochemical properties in its native and hydrolyzed forms.. The current study investigated the structural and functional characteristics of starch from ten indigenous rice varieties endemic to Northeast India.. In vitro enzymatic hydrolysis was used to reveal the dextrose equivalent profile of each type of starch. 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