Texture feature extraction using gray level gradient based co-occurence matrices

Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

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“…and GP as shown in 25,26 . Besides, GLCM considers the statistical and spatial relationship of the pixels in the image.…”

Section: Feature Extractionmentioning

confidence: 99%

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

Liu

Wang

Liu

et al. 2020

Preprint

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“…To avoid confusion with existing spatial dependence matrices like gray level co-occurrence matrix (GLCM) and gray-level gradient co-occurrence matrix (GLGCM), we will call this matrix gradient-only co-occurrence matrix (GOCM). The difference between this and GLGCM is that the values i and j in our proposed method belong to the gradient of the image, while in GLGCM i belongs to the gray-level image and j belongs to the gradient [27].…”

Section: Novel Feature Extraction Process-gocmmentioning

confidence: 99%

Feature selection for surface defect classification of extruded aluminum profiles

Chondronasios

Иван

Jordanov

2015

Int J Adv Manuf Technol

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This research investigates detection and classification of two types of the surface defects in extruded aluminium profiles; blisters and scratches. An experimental system is used to capture images and appropriate statistical features from a novel technique based on gradient-only co-occurrence matrices (GOCM) are proposed to detect and classify three distinct classes; non-defective, blisters and scratches. The developed methodology makes use of the Sobel edge detector to obtain the gradient magnitude of the image (GOCM). A comparison is made between the statistical features extracted from the original image (GLCM) and those extracted from the gradient magnitude (GOCM). This paper describes in detail every step of the image processing with example pictures illustrating the methodology. The features extracted from the image processing are classified by a two-layer feed-forward artificial neural network. The artificial neural network training is tested using different combinations of statistical features with different topologies. Features are compared individually and grouped. Results are discussed, achieving up to 98.6 % total testing accuracy.

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“…Early statistical methods for SAR image segmentation include methods based on the co-occurrence matrix (Lam 1996, Zwiggelaar 2004, Wu et al 2008. The semiautomatic algorithm proposed by Bernad et al (2007) uses the variation coefficient and co-occurrence inertia matrix for image classification into homogeneous regions.…”

Section: Introductionmentioning

confidence: 99%

Texture information-based hybrid methodology for the segmentation of SAR images

Singh

Sinha

Sikka

et al. 2011

International Journal of Remote Sensing

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Texture feature extraction using gray level gradient based co-occurence matrices

Cited by 38 publications

References 9 publications

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

Feature selection for surface defect classification of extruded aluminum profiles

Texture information-based hybrid methodology for the segmentation of SAR images

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