Nonlinear Systems Modeling &amp; Identification Using Higher Order Statistics/Polyspectra

Emara-Shabaik, Hosam E.

doi:10.1016/s0090-5267(96)80020-x

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Higher order moments can be identified as functions that use high power of a sample (higher than second-order statistics), that is opposed to the conventional first-or second-order statistics (lower order statistics). The higher order statistic provides powerful tools in identifying problems in non linear systems [26]. However, skewness and kurtosis are the examples of third-order and fourth-order statistics, respectively' [8,27].…”

Section: Higher Order Momentsmentioning

confidence: 99%

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

et al. 2022

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Background Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. Methods This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. Results According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. Conclusions This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

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Section: Higher Order Momentsmentioning

confidence: 99%

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

et al. 2022

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“…Most frequently, the orientation quantified as 0 • , 45 • , 90 • and 135 • angles and the average of the resultant values for all four directions used to extract the features [24,25] Higher order moments Higher order moments can be identified as functions that use high power of a sample (higher than 2 nd order statistics), that is opposed to the conventional first or second order statistics (lower order statistics). The higher order statistic provides powerful tools in identifying problems in non linear systems [26]. However, skewness and kurtosis are the examples of third order and fourth order statistics respectively' [8,27].…”

Section: Texture Featuresmentioning

confidence: 99%

Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques

Vijithananda

Jayatilake

Hewavithana

et al. 2022

Preprint

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Background: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.Methods: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients.The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient.At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.Results: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.Conclusion: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures such as brain biopsies.

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“…Apart from the first and second-order statistics such as mean, and variance, the higher-order statistics (HOS) have also played a tremendous role in signal processing and system analysis in recent history. The higher-order statistics are the statistical functions that use high power of sample; higher than 2nd order (lower order) statistics, provide useful tools for addressing issues in nonlinear systems [25]. Higher-order statistics such as third-order (Skewness), and fourth-order (Kurtosis) carry more useful information due to their phase sensitiveness.…”

Section: Higher Order Momentsmentioning

confidence: 99%

Texture Feature Analysis of MRI-ADC Images to Differentiate Glioma Grades Using Machine Learning Techniques.

Vijithananda

Jayatilake

Gonçalves

et al. 2022

Preprint

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Background: Apparent Diffusion Coefficient (ADC) of Magnetic Resonance Imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using Diffusion-weighted imaging (DWI). This study focuses on developing a robust Machine Learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and Grey Level Co-occurrence Matrix (GLCM) texture features of ADC. Methods: A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. Results: According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772±0.0237) performed the highest score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. Conclusion: The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

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Nonlinear Systems Modeling & Identification Using Higher Order Statistics/Polyspectra

Cited by 7 publications

References 14 publications

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques

Texture Feature Analysis of MRI-ADC Images to Differentiate Glioma Grades Using Machine Learning Techniques.

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