Comparing the Use of Sum and Difference Histograms and Gray Levels Occurrence Matrix for Texture Descriptors

Araujo, Adriel S.; Conci, Aura; Morán, Maira Beatriz Hernández; Resmini, Roger

doi:10.1109/ijcnn.2018.8489705

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…HISTO is a statistical description of discrete units, while the GLCM using second-order statistics reflects the spatial relationship of pixel gray-level values in the image (Dhruv et al, 2019). The GLDM is also based on the gray-level relationship to acquire the first-order statistics of local property values, and the GLRLM estimates the spatial relationships between groups of pixels with similar gray-level values (Araujo et al, 2018). The GLSZM can be used to compute different pixel distances, whereas the NGTDM measures the total differences in the gray level of a pixel (Thibault et al, 2014).…”

Section: Feature Extractionmentioning

confidence: 99%

Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer’s Disease

Zhang

Fang

et al. 2022

Front. Aging Neurosci.

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Background: Late-onset Alzheimer’s disease (LOAD) and early-onset Alzheimer’s disease (EOAD) are different subtypes of AD. This study aimed to build and validate radiomics models of the hippocampus for EOAD and young controls (YCs), LOAD and old controls (OCs), as well as EOAD and LOAD.Methods: Thirty-six EOAD patients, 36 LOAD patients, 36 YCs, and 36 OCs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were enrolled and allocated to training and test sets of the EOAD-YC groups, LOAD-OC groups, and EOAD-LOAD groups. Independent external validation sets including 15 EOAD patients, 15 LOAD patients, 15 YCs, and 15 OCs from Shanghai Mental Health Center were constructed, respectively. Bilateral hippocampal segmentation and feature extraction were performed for each subject, and the least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features. Support vector machine (SVM) models were constructed based on the identified features to distinguish EOAD from YC subjects, LOAD from OC subjects, and EOAD from LOAD subjects. The areas under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the models.Results: Three, three, and four features were selected for EOAD and YC subjects, LOAD and OC subjects, and EOAD and LOAD subjects, respectively. The AUC and accuracy of the SVM model were 0.90 and 0.77 in the test set and 0.91 and 0.87 in the validation set for EOAD and YC subjects, respectively; for LOAD and OC subjects, the AUC and accuracy were 0.94 and 0.86 in the test set and 0.92 and 0.78 in the validation set, respectively. For the SVM model of EOAD and LOAD subjects, the AUC was 0.87 and the accuracy was 0.79 in the test set; additionally, the AUC was 0.86 and the accuracy was 0.77 in the validation set.Conclusion: The findings of this study provide insights into the potential of hippocampal radiomic features as biomarkers to diagnose EOAD and LOAD. This study is the first to show that SVM classification analysis based on hippocampal radiomic features is a valuable method for clinical applications in EOAD.

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Section: Feature Extractionmentioning

confidence: 99%

Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer’s Disease

Zhang

Fang

et al. 2022

Front. Aging Neurosci.

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“…Since GLCM and the corresponding Haralick features retain only a relative arrangement of gray-level intensities in an image, they only encode gray-level gradients present in images. While GLCM has the advantage of a simpler interpretation than Fourier spectral analysis, its most significant shortcoming is the quadratic O(N 2 ) computational complexity (Araujo et al, 2018) compared to the faster Fourier computation with O(N log(N)) complexity (Preparata and Sarwate, 1977). Another critical shortcoming of GLCM-based methods is that both GLCM and the Haralick texture feature values depend on the number of gray levels in the quantized image (Lofstedt et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Bounds for Haralick features in synthetic images with sinusoidal gradients

Oprisan,

Oprisan

2023

Front. Signal Process.

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Introduction: The gray-level co-occurrence matrix (GLCM) reduces the dimension of an image to a square matrix determined by the number of gray-level intensities present in that image. Since GLCM only measures the co-occurrence frequency of pairs of gray levels at a given distance from each other, it also stores information regarding the gradients of gray-level intensities in the original image.Methods: The GLCM is a second-order statical method of encoding image information and dimensionality reduction. Image features are scalars that reduce GLCM dimensionality and allow fast texture classification. We used Haralick features to extract information regarding image gradients based on the GLCM.Results: We demonstrate that a gradient of k gray levels per pixel in an image generates GLCM entries on the kth parallel line to the main diagonal. We find that, for synthetic sinusoidal periodic gradients with different wavelengths, the number of gray levels due to intensity quantization follows a power law that also transpires in some Haralick features. We estimate bounds for four of the most often used Haralick features: energy, contrast, correlation, and entropy. We find good agreement between our analytically predicted values of Haralick features and the numerical results from synthetic images of sinusoidal periodic gradients.Discussion: This study opens the possibility of deriving bounds for Haralick features for targeted textures and provides a better selection mechanism for optimal features in texture analysis applications.

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On Efficient Computation of Texture Descriptors from Sum and Difference Histograms Considering the Scales of Patterns

Araújo

Conci

Morán

et al. 2018

2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)

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Comparing the Use of Sum and Difference Histograms and Gray Levels Occurrence Matrix for Texture Descriptors

Cited by 6 publications

References 10 publications

Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer’s Disease

Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer’s Disease

Bounds for Haralick features in synthetic images with sinusoidal gradients

On Efficient Computation of Texture Descriptors from Sum and Difference Histograms Considering the Scales of Patterns

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