Review



in-house matlab v.2019 toolbox  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    MathWorks Inc in-house matlab v.2019 toolbox
    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
    In House Matlab V.2019 Toolbox, 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/in-house matlab v.2019 toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    in-house matlab v.2019 toolbox - by Bioz Stars, 2026-02
    90/100 stars

    Images

    1) Product Images from "An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings"

    Article Title: An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

    Journal: Heliyon

    doi: 10.1016/j.heliyon.2024.e29602

    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.
    Figure Legend Snippet: Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.

    Techniques Used: Diffusion-based Assay



    Similar Products

    in-house matlab v.2019 toolbox  (MathWorks Inc)
    90
    MathWorks Inc in-house matlab v.2019 toolbox
    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
    In House Matlab V.2019 Toolbox, 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/in-house matlab v.2019 toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    in-house matlab v.2019 toolbox - by Bioz Stars, 2026-02
    90/100 stars
    		  Buy from Supplier
    matlab v.2019  (MathWorks Inc)
    90
    MathWorks Inc matlab v.2019
    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
    Matlab V.2019, 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/matlab v.2019/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab v.2019 - by Bioz Stars, 2026-02
    90/100 stars
    		  Buy from Supplier
    matlab script v.2019  (MathWorks Inc)
    90
    MathWorks Inc matlab script v.2019
    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
    Matlab Script V.2019, 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/matlab script v.2019/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab script v.2019 - by Bioz Stars, 2026-02
    90/100 stars
    		  Buy from Supplier
    custom-written matlab script v.2019  (MathWorks Inc)
    90
    MathWorks Inc custom-written matlab script v.2019
    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
    Custom Written Matlab Script V.2019, 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/custom-written matlab script v.2019/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    custom-written matlab script v.2019 - by Bioz Stars, 2026-02
    90/100 stars
    		  Buy from Supplier
    matlab v. 2019  (MathWorks Inc)
    90
    MathWorks Inc matlab v. 2019
    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
    Matlab V. 2019, 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/matlab v. 2019/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab v. 2019 - by Bioz Stars, 2026-02
    90/100 stars
    		  Buy from Supplier
    inhouse matlab v.2019 toolbox  (MathWorks Inc)
    90
    MathWorks Inc inhouse matlab v.2019 toolbox
    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
    Inhouse Matlab V.2019 Toolbox, 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/inhouse matlab v.2019 toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    inhouse matlab v.2019 toolbox - by Bioz Stars, 2026-02
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.

    Journal: Heliyon

    Article Title: An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

    doi: 10.1016/j.heliyon.2024.e29602

    Figure Lengend Snippet: Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.

    Article Snippet: The pathomic feature extraction was performed using an in-house MATLAB V.2019 (MathWorks, Natick, Massachusetts, USA) toolbox.

    Techniques: Diffusion-based Assay