texture descriptors using glcm Search Results


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<t>GLCM</t> <t>parameters</t> and H index obtained from the 3D spherical phantoms. (a): single spheres of different size parameter R, zero represents an invisible bar; (b): multi-sphere assembly with small spheres of variable number N; (c): two-sphere assembly with small sphere of variable distance D; (d): two-sphere assembly with small sphere of variable radius r s
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<t>GLCM</t> <t>parameters</t> and H index obtained from the 3D spherical phantoms. (a): single spheres of different size parameter R, zero represents an invisible bar; (b): multi-sphere assembly with small spheres of variable number N; (c): two-sphere assembly with small sphere of variable distance D; (d): two-sphere assembly with small sphere of variable radius r s
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<t>GLCM</t> <t>parameters</t> and H index obtained from the 3D spherical phantoms. (a): single spheres of different size parameter R, zero represents an invisible bar; (b): multi-sphere assembly with small spheres of variable number N; (c): two-sphere assembly with small sphere of variable distance D; (d): two-sphere assembly with small sphere of variable radius r s
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Image Search Results


GLCM parameters and H index obtained from the 3D spherical phantoms. (a): single spheres of different size parameter R, zero represents an invisible bar; (b): multi-sphere assembly with small spheres of variable number N; (c): two-sphere assembly with small sphere of variable distance D; (d): two-sphere assembly with small sphere of variable radius r s

Journal: EXCLI Journal

Article Title: A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images

doi: 10.17179/excli2015-723

Figure Lengend Snippet: GLCM parameters and H index obtained from the 3D spherical phantoms. (a): single spheres of different size parameter R, zero represents an invisible bar; (b): multi-sphere assembly with small spheres of variable number N; (c): two-sphere assembly with small sphere of variable distance D; (d): two-sphere assembly with small sphere of variable radius r s

Article Snippet: To validate the new heterogeneity assessment model, two types of phantoms with different distributions of gray levels assumed as SUV were created and analyzed using the Matlab software (MathWorks, Inc., Natick) for calculations of the GLCM parameters and H index.

Techniques:

The GLCM parameters and H index obtained from the 2D phantoms shown in Figure 2, the H bar in (f) is marked with its negative value of -0.017

Journal: EXCLI Journal

Article Title: A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images

doi: 10.17179/excli2015-723

Figure Lengend Snippet: The GLCM parameters and H index obtained from the 2D phantoms shown in Figure 2, the H bar in (f) is marked with its negative value of -0.017

Article Snippet: To validate the new heterogeneity assessment model, two types of phantoms with different distributions of gray levels assumed as SUV were created and analyzed using the Matlab software (MathWorks, Inc., Natick) for calculations of the GLCM parameters and H index.

Techniques:

The GLCM parameters and H index obtained from six lung cancer patients' tumors, three of them are shown in Figure 3

Journal: EXCLI Journal

Article Title: A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images

doi: 10.17179/excli2015-723

Figure Lengend Snippet: The GLCM parameters and H index obtained from six lung cancer patients' tumors, three of them are shown in Figure 3

Article Snippet: To validate the new heterogeneity assessment model, two types of phantoms with different distributions of gray levels assumed as SUV were created and analyzed using the Matlab software (MathWorks, Inc., Natick) for calculations of the GLCM parameters and H index.

Techniques: