GLCM Features Research Articles

CAD System Design for Pituitary Tumor Classification based on Transfer Learning Technique.

A brain tumor is an asymmetrical expansion by cells inevitably emulating amid them. Image processing is a vibrant research area where the handing out of the image in the medical field is an exceedingly tricky field. In this paper, an expert algorithm is suggested for the detection of pituitary brain tumors from MR images. The preprocessing techniques (smoothing, edge detection, filtering) and segmentation techniques (watershed) are applied to the online data set. The transfer learning technique is used as a classifier whose performance is measured in terms of classification accuracy. Resnet 50, Inception V3VGG16, and VGG19 models are used as classification algorithms. The proposed model is validated using different machine learning techniques considering hybrid features. 96% accuracy was obtained employing the Inception V3 model & 95% accuracy was attained using hybrid GLDS and GLCM features employing Support Vector Machine algorithm while 93% was attained using Probabilistic Neural Network and k Nearest Neighbor techniques. Computer-aided systems gave much faster and more accurate results than image processing techniques.1.0% accuracy improvement was observed while using Inception V3 over GLDS + GLCM + SVM and 2.1% accuracy improvement using GLDS + GLCM + SVM over GLDS + GLCM + kNN.

NIMG-44. RADIOMIC FEATURES OF CONTRAST ENHANCED T1 MRI SEQUENCES PREDICT SURVIVAL IN PRIMARY GLIOBLASTOMA PATIENTS WHO UNDERWENT ADJUVANT RADIATION THERAPY

Abstract PURPOSE Glioblastoma (GBM) is an aggressive primary brain tumor that is monitored through magnetic resonance imaging (MRI). Radiomic analysis offers valuable insight into better understanding the imaging features of these tumors. We tested the hypothesis that changes in radiomic features following radiation treatment predicts overall survival. Radiomic features we were interested in included 2D and 3D shape features, first order features evaluating patterns of voxel intensity within the mask regions, and higher order GLCM features. METHODS Data from 19 patients from our brain bank diagnosed with a GBM at autopsy and underwent radiation therapy were used for this study. T1C tumor masks were created by manually annotating contrast enhancing tumor for patients on pre- and post-treatment MRIs. Pyradiomics was used to extract radiomic features within both pre- and post-radiation tumor masks, including 14 shape features, 18 first order features, and 75 higher order features were compared across timepoints and the difference for each variable was correlated with overall survival. RESULTS Of the tested radiomic features, only the following shape features were found to be correlated with overall survival: Major Axis Length (R = -0.55, p =.02), Maximum 2D Diameter Row (R= -.56, p =.02), Maximum 2D Diameter Slice (R= -.52, p =.03), and Maximum 3D Diameter (R= -.47, p = .04). CONCLUSIONS These results show a decrease in the Major Axis Length, Maximum 2D Diameter, Maximum 2D Diameter, and Maximum 3D diameter of the T1C masks post-radiation correlate with an overall increase in survival. These inverse trending radiomic variables may be useful to assess tumor response to radiation therapy, specifically through assessment of shape features within the contrast-enhancing tumor region. Future research should determine how tumor changes in response to other treatment modalities and/or correlates with other prognostic variables.

Discovering pericoronary adipose tissue differences between normal and atherosclerotic coronary segments with radiomics analysis

Abstract Introduction Pericoronary adipose tissue (PCAT) extracted from coronary CT angiography (CCTA) is a novel marker for coronary inflammation. Establishing correlation between PCAT changes and the vascular lesion itself is essential for using PCAT to monitor clinical and subclinical disease status. Past studies focused on patient-level and vessel-level PCAT differences. However, lesion-level PCAT differences beyond mean attenuation values are not well understood. Purpose To investigate the differences between PCAT adjacent to atherosclerotic plaques and PCAT adjacent to normal coronary segments using radiomics analysis. Material and Methods This study enrolled 100 consecutive patients who underwent CCTA. A total of 446 plaques were successfully annotated in 89 patients. The PCAT of these 446 plaques and also their normal segment counterparts were extracted. Following prior literature, adipose tissue was defined as voxels with attenuation ranging -190 to -30 HU. PCAT was extracted within 3 mm distance from the vessel wall, along the middle 10 mm of each plaque. The PCAT of normal coronary artery segments were extracted both proximal and distal with distance D mm to the plaque with 10 mm length. We used 3 different distances to define the normal segments (D={10, 20, 30}). PyRadiomics was used to generate radiomics features and the random forest classifier from Scikit-Learn was used to distinguish PCAT extracted adjacent to atherosclerotic plaques and PCAT adjacent to normal coronary segments. To minimize bias due to vessel diameter or shape and focus on the texture of PCAT, we included only the First Order, GLCM, GLDM, GLRLM, GLSZM and NGTDM features (total 93 features) from PyRadiomics. We used 5-fold cross validation splitted at patient-level to develop the classifier. Result The radiomics model achieved acceptable discrimination for D=30 mm (mean AUROC = 0.70), borderline discrimination for D=20 mm (mean AUROC=0.68) and D=10 mm (mean AUROC = 0.60). These results suggest a trend toward greater differences between PCAT further away from the lesion compared with PCAT adjacent to the lesion. The feature "Gray Level Size Zone Matrix (GLSZM): Low Gray Level Zone Emphasis" had the highest importance in 4 out of 5 folds in our D = 30 mm experiment. The mean value of this feature is significantly different for the PCAT adjacent to the lesions compared to PCAT extracted from the normal segments (0.152 vs 0.166, p=0.001). Conclusion We investigated the difference between PCAT adjacent to atherosclerotic plaques and PCAT adjacent to normal coronary segments using radiomics analysis. We found greater discrimination when the normal segments were extracted further away from the lesion, suggesting PCAT changes due to atherosclerosis may exist beyond the length of the plaque. Moreover, "Gray Level Size Zone Matrix (GLSZM): Low Gray Level Zone Emphasis" could serve as a novel radiomic biomarker to correlate PCAT changes to atherosclerosis.

Characterization of Nanoporous Copper Using Scanning Probe Techniques

This study explores the mechanical and optical properties of nanoporous copper (NPC) films dealloyed in acid and base using qualitative and quantitative measurements. Two types of NPC films (NPC 1 and NPC 2) were prepared by dealloying precursor films in different corrosive solutions. The films were characterized using atomic force microscopy (AFM) and scattering scanning near-field optical microscopy (s-SNOM). The AFM analysis revealed distinct nanoporous structures in both films, with NPC 2 showing larger pore sizes and the presence of copper aggregations on the surface. Rugosity measures showed that NPC 2 had higher rugosity than NPC 1 and indicated fundamental differences in the height distributions of the surface nano-topography. The Minkowski connectivity analysis highlighted differences in connectedness and topological characteristics between the two samples. The analysis of the GLCM features showed clear distinctions in ASM, Contrast, Dissimilarity, Energy, and Homogeneity between NPC 1 and NPC 2. The s-SNOM results demonstrated altered optical properties, with NPC 1 showing higher contrast at 1550 nm, while NPC 2 exhibited reversed-phase contrast at 561 nm. The phase contrast at 638 nm indicated a red-shifted optical absorption peak in NPC 2 compared to NPC 1.

Comparison of Classifiers with Application on DCE-MRI and Preterm Birth

Abstract: Cervical cancer and Preterm birth in a pregnant woman is the most serious issues. The aim of our study was to compare different classifiers with SVM and its application. Various statistical methods are proposed to predict special factors. In this probability distribution is used to predict the desired outcome. However, most of the times enough information about the probability distribution is not available. In such situation, we need well predictors with minimum assumptions. Support vector machine (SVM) is a good statistical method for prediction. First order statistical features of the Brix parameters were used. First and second order features were used as explanatory variables for support vector machine (SVM) classification. Second order GLCM features could significantly predict treatment outcome with more accuracies. The performance of the classifiers is measured in terms of accuracy, specificity, and sensitivity. Finally, the result indicates the SVM classifier generates highest level accuracy, which is higher than other classifiers.

RENAL CYST DETECTION IN ABDOMINAL MRI IMAGES USING DEEP LEARNING SEGMENTATION

Renal cysts are categorized as simple cysts and complex cysts. Simple cysts are harmless and complicated cysts are cancerous and leading to a dangerous situation. The study aims to implement a deep learning-based segmentation that uses the Renal images to segment the cyst, detecting the size of the cyst and assessing the state of cyst from the infected renal image. The automated method for segmenting renal cysts from MRI abdominal images is based on a U-net algorithm. The deep learning-based segmentation like U-net algorithm segmented the renal cyst. The characteristics of the segmented cyst were analyzed using the Statistical features extracted using GLCM algorithm. The machine learning classification is performed using the extracted GLCM features. Three machine learning classifiers such as Naïve Bayes, Hoeffding Tree and SVM are used in the proposed study. Naive Bayes and Hoeffding Tree achieved the highest accuracy of 98%. The SVM classifier achieved 96% of accuracy. This study proposed a new system to diagnose the renal cyst from MRI abdomen images. Our study focused on cyst segmentation, size detection, feature extraction and classification. The three-classification method suits best for classifying the renal cyst. Naïve Bayes and Hoeffding Tree classifier achieved the highest accuracy. The diameter of cyst size is measured using the blobs analysis method to predict the renal cyst at an earlier stage. Hence, the deep learning-based segmentation performed well in segmenting the renal cyst and the three classifiers achieved the highest accuracy, above 95%.

Image Classification of Tempe Fermentation Maturity Using Naïve Bayes Based on Linear Discriminant Analysis

One of the foods in Indonesia that has a lot of nutritional content and benefits, one of which is tempeh. Tempe is usually made by fermenting soybeans with mold under special conditions to become tempeh. In the fermentation process, tempeh producers need to monitor the maturity of the tempeh until it is suitable for consumption. To detect this maturity requires a separate effort, so that an image processing approach is proposed in this study with the support of feature selection. An image allows for various features to be taken, such as texture features using GLCM and various color features including RGB, HSV, LAB, CMYK, YUV, HCL, HIS, LCH. With so many features, it is necessary to do a selection so that computation in its classification becomes efficient. This study aims to classify tempeh fermented images using the Naive Bayes method with Linear Discriminant Analysis (LDA)feature selection for GLCM features and eight color features. Tempe fermentation image is divided into three classes, namely raw, ripe and rotten. Based on the experimental results, the average accuracy in the test is 84.06%. In testing the fastest time is 1.87 seconds and the longest is 2.20 seconds. This shows that the classification of fermented tempeh maturity with Naive Bayes with LDA feature selection can work well.

Smart Agriculture Framework for Automated Detection of Leaf Blast Disease in Paddy Crop Using Colour Slicing and GLCM Features based Random Forest Approach

Smart Agriculture Framework for Automated Detection of Leaf Blast Disease in Paddy Crop Using Colour Slicing and GLCM Features based Random Forest Approach

Brain Tumor Detection Based on A Combination of GLCM and LBP Features with PCA and IG

Brain Tumor Detection Based on A Combination of GLCM and LBP Features with PCA and IG

Effects of non-stationary blur on texture biomarkers of bone using Ultra-High Resolution CT.

To advance the development of radiomic models of bone quality using the recently introduced Ultra-High Resolution CT (UHR CT), we investigate inter-scan reproducibility of trabecular bone texture features to spatially-variant azimuthal and radial blurs associated with focal spot elongation and gantry rotation. The UHR CT system features 250×250 μm detector pixels and an x-ray source with a 0.4×0.5 mm focal spot. Visualization of details down to ~150 μm has been reported for this device. A cadaveric femur was imaged on UHR CT at three radial locations within the field-of-view: 0 cm (isocenter), 9 cm from the isocenter, and 18 cm from the isocenter; we expect the non-stationary blurs to worsen with increasing radial displacement. Gray level cooccurrence (GLCM) and gray level run length (GLRLM) texture features were extracted from 237 trabecular regions of interest (ROIs, 5 cm diameter) placed at corresponding locations in the femoral head in scans obtained at the different shifts. We evaluated concordance correlation coefficient (CCC) between texture features at 0 cm (reference) and at 9 cm and 18 cm. We also investigated whether the spatially-variant blurs affect K-means clustering of trabecular bone ROIs based on their texture features. The average CCCs (against the 0 cm reference) for GLCM and GLRM features were ~0.7 at 9 cm. At 18 cm, the average CCCs were reduced to ~0.17 for GLCM and ~0.26 for GLRM. The non-stationary blurs are incorporated in radiomic features of cancellous bone, leading to inconsistencies in clustering of trabecular ROIs between different radial locations: an intersection-over-union overlap of corresponding (most similar) clusters between 0 cm and 9 cm shift was >70%, but dropped to <60% for the majority of corresponding clusters between 0 cm and 18 cm shift. Non-stationary CT system blurs reduce inter-scan reproducibility of texture features of trabecular bone in UHR CT, especially for locations >15 cm from the isocenter. Radiomic models of bone quality derived from UHR CT measurements at isocenter might need to be revised before application in peripheral body sites such as the hips.

BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.&#x0D;

Wire rope defect identification based on ISCM-LBP and GLCM features

Wire rope defect identification based on ISCM-LBP and GLCM features

Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT.

This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.

Sputum smears quality inspection using an ensemble feature extraction approach.

The diagnosis of tuberculosis (TB) is extremely important. Sputum smear microscopy is thought to be the best method available in terms of accessibility and ease of use in resource-constrained countries. In this paper, research was conducted to evaluate the effectiveness of tuberculosis diagnosis by examining, among other things, the underlying causes of sputum smear quality for Ethiopian states such as Tigray, Amahira, and Oromia. However, because it is done manually, it has its limitations. This study proposes a model for sputum smear quality inspection using an ensemble feature extraction approach. The dataset used was recorded and labeled by experts in a regional lab in Bahir Dar, near Felege Hiwot Hospital after being collected from Gabi Hospital, Felege Hiwot Hospital, Adit Clinic and Gondar Hospital, as well as Kidanemihret Clinic in Gondar. We used a controlled environment to reduce environmental influences and eliminate variation. All the data was collected using a smartphone (the standard 15) with a jpg file extension and a pixel resolution of 1,728 × 3,840. Prior to feature extraction, bicubic resizing, and ROI extraction using thresholding was performed. In addition, sequential Gaussian and Gabor filters were used for noise reduction, augmentation, and CLAHE was used for enhancement. For feature extraction, GLCM from the gray label and CNN from the color image were both chosen. Ultimately, when CNN, SVM, and KNN classifiers were used to test both CNN and GLCM features, KNN outperformed them all with scores of 87, 93, and 94% for GLCM, CNN, and a hybrid of CNN and GLCM, respectively. CNN with GLCM outperformed other methods by 0.7 and 0.1% for GLCM and CNN feature extractors using the same classifier, respectively. In addition, the KNN classifier with the combination of CNN and GLCM as feature extractors performed better than existing methods by 1.48%.

Performance Evaluation Of Variations Boosting Algorithms For Classifying Formalin Fish From Photos

Fish is one of the foods that are often consumed by humans to complete the protein in the body. Indonesia is rich in animal protein so some rogue traders to avoid losses due to rotten fish, sellers process fish to make it look fresh by fishing with formalin liquids so that buyers think the fish is still fresh, this problem is often found in the market so that it takes the right solution. The solution offered is to apply Machine Learning (Boosting Algorithm) to classify fresh fish and fish that have been planned with formalin by utilizing the extraction of GLCM features. The findings of this study indicate a variation of boosting algorithms can provide solutions to this problem. Accuracy, precision, recall, f1-score, f2-score, and Jaccard score in tilapia fish with a value of 0.95, 0.95, 0.95, 0.95, 0.95, 0.95 this result is obtained by extreme gradient boosting variations with the highest achievements compared to variations Other things are also similar to Tamban fish with a value of 0.78, 0.775, 0.78, 0.825, 0.77, 0.632. The effect of boosting algorithm on the measurement of model performance looks very increased in tilapia fish while in tamban fish do not provide maximum results, but these findings are better

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm.

As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.

Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer.

To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints. This retrospective study included breast cancer patients (n = 117) who underwent neoadjuvant chemotherapy. Data types used included tumor ADC values, diffusion-weighted and dynamic-contrast-enhanced MRI at three treatment timepoints, and patient demographics and tumor data. GLCM textural analysis was performed on MRI data. An extreme gradient boosting machine learning algorithm was used to predict pCR. Prediction performance was evaluated using the area under the curve (AUC) of the receiver operating curve along with precision and recall. Prediction using texture features of DWI and DCE images at multiple treatment time points (AUC = 0.871; 95% CI: (0.768, 0.974; p<0.001) and (AUC = 0.903 95% CI: 0.854, 0.952; p<0.001) respectively), outperformed that using mean tumor ADC (AUC = 0.850 (95% CI: 0.764, 0.936; p<0.001)). The AUC using all MRI data was 0.933 (95% CI: 0.836, 1.03; p<0.001). The AUC using non-MRI data was 0.919 (95% CI: 0.848, 0.99; p<0.001). The highest AUC of 0.951 (95% CI: 0.909, 0.993; p<0.001) was achieved with all MRI and all non-MRI data at all time points as inputs. Using XGBoost on extracted GLCM features and non-imaging data accurately predicts pCR. This early prediction of response can minimize exposure to toxic chemotherapy, allowing regimen modification mid-treatment and ultimately achieving better outcomes.

Screening of COVID-19 Based on GLCM Features from CT Images Using Machine Learning Classifiers.

In healthcare, the decision-making process is crucial, including COVID-19 prevention methods should include fast diagnostic methods. Computed tomography (CT) is used to diagnose COVID patients' conditions. There is inherent variation in the texture of a CT image of COVID, much like the texture of a CT image of pneumonia. The process of diagnosing COVID images manually is difficult and challenging. Using low-resolution images and a small COVID dataset, the extraction of discriminant characteristics and fine-tuning of hyperparameters in classifiers provide challenges for computer-assisted diagnosis. In radiomics, quantitative image analysis is frequently used to evaluate the prognosis and diagnose diseases. This research tests an ML model built on GLCM features collected from chest CT images to screen for COVID-19. In this study, Support Vector Machines, K-nearest neighbors, Random Forest, and XGBoost classifiers are used together with LBGM. Tuning tests were used to regulate the hyperparameters of the model. With cross-validation, tenfold results were obtained. Random Forest and SVM were the best classification methods for GLCM features with an overall accuracy of 99.94%.The network's performance was assessed in terms of sensitivity, accuracy, and specificity.

A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm.

The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial images, this work explored an enhanced particle swarm algorithm to optimize the parameters of the support vector machine. A support vector machine model was therefore established for the identification of the normal and defective states of the insulators. This methodology works with the structure minimization principle of SVM and the characteristics of particle swarm fast optimization. First, the aerial insulator image was segmented as a target by way of the seed region growth based on double-layer cascade morphological improvements, and then, HOG features plus GLCM features were extracted as sample data. Finally, an ameliorated PSO-SVM classifier was designed to realize insulator state identification. Comparisons were made between PSO-SVM and conventional machine learning algorithms, SVM and Random Forest, and an optimization algorithm, Gray Wolf Optimization Support Vector Machine (GWO-SVM), and advanced neural network CNN. The experimental results showed that the performance of the algorithm proposed in this paper touched the top level, where the recognition accuracy rate was 92.11%, the precision rate 90%, the recall rate 94.74%, and the F1-score 92.31%.

Classification and Segmentation of MRI Brain Images using Support Vector Machine and Fuzzy C-means Clustering

An early diagnosis of brain disorders is very important for timely treatment of such diseases.Several imaging modalities are used to capture the anomalities by obtaining either the physiological or morphological information. The scans obtained using imaging modalities such as magnetic resonance imaging (MRI) are investigated by the radiologists in order to diagnose the diseases. However such investigations are time consuming and might involve errors. In this paper, a fuzzy c-means clustering method is used for brain MRI image segmentation.The GLCM features are obtained from the segmented images and are subsequently mapped in to a PCA space. A support vector machine (SVM) classifier is used to classify brain MRI images taken from BRATS-13 images. The method is evaluated by employing various performance measures such as Jaccard index, Dice index, mean square error (MSE), peak signal to noise ratio (PSNR). The results show that the method outperforms the existing methods.