Gray Level Run Length Matrix Research Articles

Robustness of [18F]FDG PET/CT radiomic analysis in the setting of drug-induced cardiotoxicity

Background and objectivesStandardization of radiomic data acquisition protocols is still at a very early stage, revealing a strong need to work towards the definition of uniform image processing methodologies The aim of this study is to identify sources of variability in radiomic data derived from image discretization and resampling methodologies prior to image feature extraction. Furthermore, to identify robust potential image-based biomarkers for the early detection of cardiotoxicity. MethodsImage post-acquisition processing, interpolation, and volume of interest (VOI) segmentation were performed. Four experiments were conducted to assess the reliability in terms of the intraclass correlation coefficient (ICC) of the radiomic features and the effects of the variation of voxel size and gray level discretization. Statistical analysis was performed separating the patients according to cardiotoxicity diagnosis. Differences of texture features were studied with Mann-Whitney U test. P-values <0.05 after multiple testing correction were considered statistically significant. Additionally, a non-supervised k-Means clustering algorithm was evaluated. ResultsThe effect of the variation in the voxel size demonstrated a non-dependency relationship with the values of the radiomic features, regardless of the chosen discretization method. The median ICC values were 0.306 and 0.872 for absolute agreement and consistency, respectively, when varying the discretization bin number. The median ICC values were 0.678 and 0.878 for absolute agreement and consistency, respectively, when varying the discretization bin size. A total of 16 first order, 6 Gray Level Co-occurrence Matrix (GLCM), 4 Gray Level Dependence Matrix (GLDM) and 4 Gray Level Run Length Matrix (GLRLM) features demonstrated statistically significant differences between the diagnosis groups for interim scans (P<0.05) for the fixed bin size (FBS) discretization methodology. However, no statistically significant differences between diagnostic groups were found for the fixed bin number (FBN) discretization methodology. Two clusters based on the radiomic features were identified. ConclusionsGray level discretization has a major impact on the repeatability of the radiomic features. The selection of the optimal processing methodology has led to the identification of texture-based patterns for the differentiation of early cardiac damage profiles.

Assessment of short-term effect of platelet-rich plasma treatment of tendinosis using texture analysis of ultrasound images.

Computer-aided diagnosis (i.e., texture analyses) tools are becoming increasingly beneficial methods to monitor subtle tissue changes. The aim of this pilot study was to investigate short-term effect of platelet rich plasma (PRP) treatment in supraspinatus and common extensor of the forearm tendinosis by using texture analysis of ultrasound (US) images as well as by clinical questionnaires. Thirteen patients (7 male and 6 female, age 36-60 years, mean age 51.2 ± 5.2) were followed after US guided PRP treatment for tendinosis of two tendons (9 patients with lateral epicondylitis and 4 with supraspinatus tendinosis). Clinical and US assessment was performed prior to as well as 3 months after PRP treatment with validated clinical questionnaires. Tissue response in tendons was assessed by using gray level run length matrix method (GLRLM) of US images. All patients improved of tendinosis symptoms after PRP treatment according to clinical questionnaires. Almost all GLRLM features were statistically improved 3 months after PRP treatment. GLRLM-long run high gray level emphasis (LRLGLE) revealed the best moderate positive and statistically significant correlation after PRP (r = 0.4373, p = 0.0255), followed by GLRLM-low gray level run emphasis (LGLRE) (r = 0.3877, p = 0.05). Texture analysis of tendinosis US images was a useful quantitative method for the assessment of tendon remodeling after minimally invasive PRP treatment. GLRLM features have the potential to become useful imaging biomarkers to monitor spatial and time limited tissue response after PRP, however larger studies with similar protocols are needed.

Feasibility of delta radiomics–based pCR prediction for rectal cancer patients treated with magnetic resonance–guided adaptive radiotherapy

Magnetic resonance–guided adaptive radiotherapy (MRgART) represents the latest frontier in precision radiotherapy. It is distinguished from other modalities by the possibility of acquiring high-contrast soft tissue images, combined with the ability to recalculate and re-optimize the plan on the daily anatomy. The extensive database of available images offers ample scope for using disciplines such as radiomics to try to correlate features and outcomes. This study aimed to correlate the change of radiomics feature along the treatment to pathological complete response (pCR) for locally advanced rectal cancer (LARC) patients. Twenty-eight LARC patients undergoing neoadjuvant chemoradiotherapy (nCRT) with a short course (25 Gy, 5 Gy × 5f) MRgART at 1.5 Tesla MR-Linac were enrolled. The T2-weighted images acquired at each fraction, corresponding target delineation, pCR result of the surgical specimen, and clinical variables were collected. Seven families of features [First Order, Shape, Gray-level Co-occurrence Matrix (GLCM), Gray-level Dependence Matrix (GLDM), Gray-level Run Length Matrix (GLRLM), Gray-level Size Zone Matrix (GLSZM), and Neighborhood Gray Tone Difference Matrix (NGTDM)] were extracted, and delta features were calculated from the ratio of features of each successive fraction to those of the first fraction. Mann-Whitney U test and LASSO were utilized to reduce the dimension of features and select those features that are most significant to pCR. At last, the radiomics signatures were established by linear regression with the final set of features and their coefficients. A total of 581 radiomics features were extracted, and 2,324 delta features were calculated for each patient. Nineteen features and delta features, and one clinical variable (cN) were significant (p&amp;lt; 0.05) to pCR; seven predictive features were further selected and included in the linear regression to construct the radiomics signature significantly discriminating pCR and non-pCR groups (p&amp;lt; 0.05). Delta features based on MRI images acquired during a short course MRgART could potentially be used to predict treatment response in LARC patients undergoing nCRT.

Optimization of Intracranial Hemorrhage Using CT Scan Images and Feature Extraction

Intracranial bleeding is among the most severe forms of brain stroke. The neurologic effects and artery rupture cause bleeding in the brain and the tissue around it. Haemorrhage is classified based on where the bleeding occurs on the brain. This paper depicts the application of multiple machine-learning approaches to separate CT scan images into normal and pathological categories. Separate analysis is conducted on the functionality of the features extracted from the various texturing approaches, such as the Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run-Length Matrix (GRLM). Particle Swarm Optimisation (PSO) and K-Nearest Neighbours are used to choose relevant characteristics that increase the classification accuracy for feature extraction. The findings demonstrate that these texture features have excellent discrimination accuracy

Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner.

18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner. A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability. In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features. Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.

Machine learning in the detection of dental cyst, tumor, and abscess lesions

Background and ObjectiveDental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions.Materials & MethodsThe current retrospective study retrieved a total of 172 dental panoramic radiographs with lesion including dental cysts, tumors, or abscess. Radiographs that failed to meet technical criteria for diagnostic quality (such as significant overlap of teeth, a diffuse image, or distortion) were excluded from the sample. The methodology adopted in the study comprised of five stages. At first, the radiographs are improved, and the area of interest was segmented manually. A variety of feature extraction techniques, such GLCM, GLRLM, and the wavelet analysis were used to gather information from the area of interest. Later, the lesions were classified as a cyst, tumor, abscess, or using a support vector machine (SVM) classifier. Eventually, the data was transferred into a Microsoft Excel spreadsheet and statistical package for social sciences (SPSS) (version 21) was used to conduct the statistical analysis. Initially descriptive statistics were computed. For inferential analysis, statistical significance was determined by a p value < 0.05. The sensitivity, specificity, and accuracy were used to find the significant difference between assessed and actual diagnosis.ResultsThe findings demonstrate that 98% accuracy was achieved using GLCM, 91% accuracy using Wavelet analysis & 95% accuracy using GLRLM in distinguishing between dental cyst, tumor, and abscess lesions. The area under curve (AUC) number indicates that GLCM achieves a high degree of accuracy. The results achieved excellent accuracy (98%) using GLCM.ConclusionThe GLCM features can be used for further research. After improving the performance and training, it can support routine histological diagnosis and can assist the clinicians in arriving at accurate and spontaneous treatment plans.

General Image Manipulation Detection Using Feature Engineering and a Deep Feed-Forward Neural Network

Within digital forensics, a notable emphasis is placed on the detection of the application of fundamental image-editing operators, including but not limited to median filters, average filters, contrast enhancement, resampling, and various other operations closely associated with these techniques. When conducting a historical analysis of an image that has potentially undergone various modifications in the past, it is a logical initial approach to search for alterations made by fundamental operators. This paper presents the development of a deep-learning-based system designed for the purpose of detecting fundamental manipulation operations. The research involved training a multilayer perceptron using a feature set of 36 dimensions derived from the gray-level co-occurrence matrix, gray-level run-length matrix, and normalized streak area. The system detected median filtering, mean filtering, the introduction of additive white Gaussian noise, and the application of JPEG compression in digital Images. Our system, which utilizes a multilayer perceptron trained with a 36-feature set, achieved an accuracy of 99.46% and outperformed state-of-the-art deep-learning-based solutions, which achieved an accuracy of 97.89%.

Proposing new seismic texture attributes based on novel gray level matrix with application to salt dome detection

The exploration of new hydrocarbon resources requires a detailed image of the subsurface geological structures. Interpreting seismic sections is one of the most common ways of accurately imaging the Earth's subsurface. Automated seismic section interpretation requires accurate delineation of the target geobody through seismic section segmentation. Texture analysis of images is one of the common tools for seismic section segmentation for target geobody identification. Exploration of geological phenomena e.g. the salt dome, buried channels, etc., is very important in the field of hydrocarbon exploration and production due to the possibility of creating stratigraphic and structural hydrocarbon traps, creating potential for subsurface energy storage and drilling hazards. They have textural differences with their surrounding environment, and therefore the analysis of seismic sections using textural attributes to determine the geometry of these events is one of the challenges facing interpreters. Gray level co-occurrence matrix (GLCM) is the commonly used tool for textural analysis of seismic images, based on which several attributes have been introduced. Optimal adjustment of numerous input parameters in GLCM attributes and their strong dependence on the dip of events are the drawbacks of this method. In this paper, we proposed new seismic attributes based on the newly introduced gray level matrices (GLM) consisting of gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), gray level difference matrix (GLDM), and normalized gray level dependence matrix (NGLDM). The new proposed seismic attributes depend on fewer input parameters for adjustment than conventional attributes while increasing accuracy in event detection, and even GLSZM-based attributes are independent of the phenomena dip. The efficiency of the proposed attributes was evaluated on the real field and synthetic seismic data containing a salt dome and its results were compared with conventional GLCM-based attributes. The qualitative and quantitative results showed that in addition to the methodological superiority of the newly introduced gray level matrices compared to the GLCM, the accuracy of the proposed attributes was also increased in the salt dome detection. Moreover, it seems that the linear transform to grayscale performed better than the non-linear one in distinguishing the salt dome from the surrounding sediments. But the main challenge is distinguishing the salt dome texture from the weak layering which the nonlinear transform has done better than the linear one.

Magnetic susceptibility and R2*-based texture analysis for evaluating liver fibrosis in chronic liver disease

PurposeTo explore potential feasibility of texture features in magnetic susceptibility and R2* maps for evaluating liver fibrosis. MethodsThirty-one patients (median age 46 years; 22 male) with chronic liver disease were prospectively recruited and underwent magnetic resonance imaging (MRI), blood tests, and liver biopsy. Susceptibility and R2* maps were obtained using a 3-dimensional volumetric interpolated breath-hold examination sequence with a 3T MRI scanner. Texture features, including histogram, gray-level co-occurrence matrix (GLCM), gray-level dependence matrix (GLDM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and neighboring gray tone difference matrix (NGTDM) features, were extracted. Texture features and blood test results of non-significant (Ishak-F < 3) and significant fibrosis patients (Ishak-F ≥ 3) were compared, and correlations with Ishak-F stages were analyzed. Areas under the curve (AUCs) were calculated to determine the efficacy for evaluating liver fibrosis. ResultsNine texture features of susceptibility maps and 19 features of R2* maps were significantly different between non-significant and significant fibrosis groups (all P < 0.05). Large dependence high gray-level emphasis (LDHGLE) of GLDM and long run high gray-level emphasis (LRHGLE) of GLRLM in R2* maps showed significantly negative and good correlations with Ishak-F stages (r = –0.616, P < 0.001; r = –0.637, P < 0.001). Busyness (NGTDM) in susceptibility maps, LDHGLE of GLDM and LRHGLE of GLRLM in R2* maps yield the highest AUCs (AUC = 0.786, P = 0.007; AUC = 0.807, P = 0.004; AUC = 0.819, P = 0.003). ConclusionTexture characteristics of susceptibility and R2* maps revealed possible staging values for liver fibrosis. Susceptibility and R2*-based texture analysis may be a useful and noninvasive method for staging liver fibrosis.

The Role of Radiomics Model to Predict Early Xerostomia in Head and Neck Cancer after Radiotherapy

Based on the patients' characteristics, the most treated patients suffering from early xerostomia during radiation therapy treatment and in the early months after treatment in head and neck cancer. The aim of this study is to provide an image-based model to predict early xerostomia in these patients before radiotherapy. The dosimetric and clinical factors (age, sex, history of disease) and computed tomography (CT) features are also combined with the image data in resolving xerostomia detection. Clinical factors and CT features were considered for 62 patients with head and neck cancer, and the complication of parotid and salivary glands was evaluated. For feature extraction from CT images, 3D Slicer software was used to extract 107 features. The features type included shape, gray level dependence matrix, gray level co-occurrence matrix, first order gray level run length matrix, gray level size zone matrix, and neighborhood gray-tone difference matrix. Four different datasets; demographic + sum dose volume histogram (DVH), demographic + ipsilateral and contralateral DVH, demographic + CT features + sum DVH, and demographic + CT features + ipsilateral and contralateral DVH; were used as model inputs. Four algorithms: Random Tree (RT), Linear Support Vector Machine (LSVM), Neural Network (NN), and Bayesian Network (BN), were applied to analyze the datasets to predict xerostomia. The metrics of the models used sensitivity, specificity, accuracy, and area-under-the-curve (AUC) to assess the performance. The AUC has its highest value for the RT model among the aforementioned models and its value for the first and second data sets were 0.84 and 0.64, respectively. In general, model performance was improved when the sum of the parotids DVH data was used. Furthermore, the RT model showed the best performance when using radiomics features in the third and fourth data sets with the AUC of 0.86 and 0.89, respectively. Among the RT, LSVM, NN, and BN models, RT has the highest performance in predicting radiation-induced early xerostomia in patients who underwent head and neck radiotherapy. Model performance was improved when the sum of the parotids DVH data was used in addition to the dose data and CT features. This study showed that combining the dosimetric data with radiomics features may improve the model performance for predicting early xerostomia.

Prognostic Value of Weekly Delta-Radiomics during MR-Linac Radiotherapy of Glioblastoma

MRI after chemoradiotherapy (chemoRT) shows areas of presumed tumor growth in ≤ 50% of glioblastoma (GBM) patients, which can be true progression (TP) - tumor growth with poor treatment response, or pseudoprogression (PP) - edema and tumor necrosis with favorable treatment response. Patients with TP have median overall survival (OS) of only 7 months, while patients with PP have median OS of 36 months. However, on imaging, TP and PP are usually not discernible during treatment, making it difficult to adapt radiation for poor responders. The purpose of this study was to investigate the prognostic value of delta radiomic features from MR-Linac for GBM. Using an IRB-approved prospective cohort of GBM patients undergoing 30 fractions of chemoRT to 60 Gy on a 0.35T MR-Linac, 2 regions of interest (ROI) were contoured on daily T2-weighted treatment set-up scans: 1) tumor/edema (lesion) and 2) post-surgical resection cavity (RC). The lesion ROI were used to calculate texture features: second order radiomics features based on the gray-level co-occurrence matrix (GLCM), gray-level size zone matrix (GLSZM), gray-level run length matrix (GLRLM), and neighborhood gray-tone difference matrix (NGTDM). Each of these describe the probability of spatial relationships of gray levels occurring within the ROI. Features from fraction 1 (pre-radiation) were subtracted from fractions 5, 10, 15, 25, and 30 to create delta features at 5 timepoints (D5-D30). Patient response was retrospectively defined as no progression (NP), TP, or PP. Supervised machine learning was utilized using a 500-tree random forest (RF) classification model with TP or PP as the outcome. Variable importance analysis was conducted by calculating the out-of-bag errors with multiple bootstrapped data sets. The most prognostic features were selected using the RF importance scores. Thirty-six patients were screened for inclusion: 9 were excluded due to no T2 lesion (RC ROI only). Of the remaining 27 patients: 10 had NP, 11 had TP, and 6 had PP. Thirty-nine texture features, plus lesion volume and mean lesion intensity (for a total of 41 variables per time point) were calculated and included in the model. Of the 10 most prognostic features, 6 were from D10, suggesting that prognostic changes in the underlying lesion microenvironment are occurring within the first 10 fractions of treatment. The model selected GLSZM high gray-level zone emphasis (HGZE) D10, IBSI code 5GN9, as the most prognostic feature. The receiver operator characteristic (ROC) area under the curve (AUC) for GLSZM HGZE D10 was 0.94 (95% CI = 0.81-1.00). Delta radiomic features extracted from MR-Linac imaging may predict between PP and TP in GBM patients during treatment, which is earlier than current methods. This could allow physicians to adapt/intensify treatment in real time for poorly responding patients. Future directions include analysis with a larger patient cohort and with additional MRI contrasts (MR-Linac multiparametric MRI).

Enhanced mammogram classification with convolutional neural network: An improved algorithm for automated breast cancer detection

The current use of medical image processing can be employed to detect various diseases or tumors present in the human body. Breast cancer, which predominantly affects women and can result in death, can be identified at an early stage using mammography, a special screening method of computer tomography (CT) scan, which may potentially save the patient's life. Despite advancements in medical imaging technology, radiologists encounter difficulties in precisely discriminating between non-cancerous and cancerous brain tumors owing to their cellular composition. This study aims to solve the issue of automatic detection of breast cancer utilizing efficient image processing methods. The quality of mammography pictures is increased using Non-Local-Means (NLM) filtering, adaptive histogram equalization (AHE), and clustering with a morphological technique. The Gray Level Run Length Matrix (GLRLM), which considers the shape, surface, and location of the breast tumour cells, is utilized to extract important details from the mammography image. The K-nearest neighbor (K-NN) method is used to categorize the image as normal or abnormal based on the selected attributes. Using a kernel-based convolutional neural network, the tumour is then isolated from the aberrant image and categorized as benign or malignant (KBCNN). This approach has demonstrated improved precision compared to existing methods, achieving more than 95% accuracy.

Nazofarenks karsinomu olan hastalarda metastatik lenf nodlarının saptanmasında manyetik rezonans görüntüleme doku analizi

Abstract Aims: To investigate the role of magnetic resonance imaging (MRI) texture analysis (TA) in the detection of metastatic lymph nodes in patients with nasopharyngeal carcinoma (NPC). Material and methods: Between January 2020 and October 2021, 15 NPC patients with 32 metastatic lymph nodes and 30 healthy subjects with benign lymph nodes were included in the study. The texture features compared between metastatic and benign lymph nodes. The independent predictor parameters of metastatic lymph nodes were determined using multivariate regression analysis. Receiver operator characteristics (ROC) analysis was used to evaluate the diagnostic performance of the regression models. Results: The first order texture features did not differ significantly between groups (p&amp;gt;0.05). Except for correlation in metastatic lymph nodes, all gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) features were significantly different (p&amp;lt;0.05). The GLCM features of joint entropy, joint energy, and maximum probability; and the GLRLM features of gray level non uniformity and low gray level run emphasis were independent predictors of metastatic lymph nodes. The area under the curve (AUC) values for the GLCM regression model and GLRLM regression model were 0.975 and 0.928, respectively. Conclusion: MRI texture analysis may be useful to detect metastatic lymph nodes in patients with NPC by providing quantitative information on tissue heterogeneity and cellular composition.

Introducing radiomics model to predict active plaque in multiple sclerosis patients using magnetic resonance images

Multiple Sclerosis (MS) is the most common non-traumatic disabling disease in young people. The prediction active plaque has the potential to offer new biomarkers for assessing the activity of MS disease. Consequently it supports patient management in the clinical setting and trials. This study aims to investigate the predictive capability of radiomics features for identifying active plaques in these patients using T2 FLAIR (Fluid Attenuated Inversion Recovery) images. For this purpose, a dataset images from 82 patients with 122 lesions was analyzed. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. Six different classifier algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), were employed for modeling. The models were evaluated using 5-fold cross-validation, and performance metrics including sensitivity, specificity, accuracy, area under the curve (AUC), and mean squared error were computed. A total of 107 radiomics features were extracted for each lesion, and 11 robust features were identified through the feature selection process. These features consisted of four shape features (elongation, flatness, major axis length, mesh volume), one first-order feature (energy), one Gray Level Co-occurrence Matrix feature (correlation), two Gray Level Run Length Matrix features (gray level non-uniformity, gray level non-uniformity normalized), and three Gray Level Size Zone Matrix features (low gray level zone emphasis, size zone non-uniformity, small area low gray level emphasis). The NB classifier demonstrated the best performance with an AUC, sensitivity, and specificity of 0.85, 0.82, and 0.66, respectively. The findings indicate the potential of radiomics features in predicting active MS plaques in T2 FLAIR images.

Computerized Tomography Texture Analysis in the Differential Diagnosis of Intracranial Epidermoid and Arachnoid Cysts.

This study evaluated the differences between arachnoid and epidermoid cysts in computerized tomography (CT) texture analysis (TA). The study included 12 patients with intracranial epidermoid cysts and 26 patients with intracranial arachnoid cysts who were diagnosed with diffusion-weighted magnetic resonance imaging (DW-MRI) and who had undergone an unenhanced CT examination before treatment. The LIFEx application software was used to obtain texture features. Eighty-two texture features from 38 lesions were automatically calculated for each lesion. The Shapiro-Wilk test was used to test the normality of the scores, and the Mann-Whitney U Test was used to test the difference between the groups. Receiver operating characteristic (ROC) curves and multivariate logistic regression modeling examined the parameters' diagnostic performances. The median age of the patients was 53 years (range: 19-88 years). Eighty-two texture parameters were evaluated in the first order: gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), neighbor gray-tone difference matrix (NGTDM), and gray-level size zone matrix (GLSZM) groups. There was a statistically significant difference between the arachnoid cyst and the epidermoid cyst in the variables of compacity, compactness 1, compactness 2, sphericity, asphericity, sum average, coarseness, and low gray-level zone (p<0.05). According to the multiple logistic regression model, it was determined that the sum average in the GLCM group (B=-0.11; p=0.015), coarseness (B= 869.5; p=0.044) in the NGTDM group, and morphological sphericity (B=24.18; p=0.047) were the radiomics variables that increased the probability of epidermoid diagnosis. According to the classification table of the model, the sensitivity rate was found to be 83%, and the specificity rate was found to be 96%. Therefore, the probability of accurate model classification was 92%. CT TA is a method that can be applied with high diagnostic accuracy in the differential diagnosis of intracranial epidermoid and arachnoid cysts, especially in patients who cannot undergo an MRI examination.

Design and Experimental Research of Intelligent Inspection and Classification System for Yuba Skin Quality

At present, the surface quality of Yuba skin is determined by sensory methods. In order to realize the intelligent classification detection of Yuba skin quality, this study designed a system that automatically determines the quality of Yuba skin surfaces based on image processing and support vector machine (SVM) approaches. Specifically, the system uses image preprocessing to extract the grayscale eigenvalues, gray level co-occurrence matrix (GLCM) eigenvalues, and gray level run length matrix (GLRLM) eigenvalues of the sample image and uses them as input values for a quality grading system. Through model evaluation of three classification models, the SVM classification model was selected according to the evaluation results, and different kernel functions were used in the model for sample training. Based on Matlab, the quality grading software of Yuba skin was developed and designed. Intelligent detection and grading were realized through the radial basis kernel function support vector machine (RBF-SVM) grading model. The best penalty factor (c = 3.50) and kernel parameter value (g = 0.98) were obtained through cross-validation. The accuracy of the model was 95.31% and 94.16% for the training and test sets, respectively. The grading accuracy of the RBF-SVM grading system was 93.56%, and the error was less than 5% compared with the traditional sensory method of grading; thus, the quality classification method based on the SVM classification system for Yuba skin is feasible and can be used for quality detection.

A machine learning-based sonomics for prediction of thyroid nodule malignancies.

This study aims to use ultrasound derived features as biomarkers to assess the malignancy of thyroid nodules in patients who were candidates for FNA according to the ACR TI-RADS guidelines. Two hundred and ten patients who met the selection criteria were enrolled in the study and subjected to ultrasound-guided FNA of thyroid nodules. Different radiomics features were extracted from sonographic images, including intensity, shape, and texture feature sets. Least Absolute Shrinkage and Selection Operator (LASSO), Minimum Redundancy Maximum Relevance (MRMR), and Random Forests/Extreme Gradient Boosting Machine (XGBoost) algorithms were used for feature selection and classification of the univariate and multivariate modeling, respectively. Evaluation of models performed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). In the univariate analysis, Gray Level Run Length Matrix - Run-Length Non-Uniformity (GLRLM-RLNU) and gray-level zone length matrix - Run-Length Non-Uniformity (GLZLM-GLNU) (both with an AUC of 0.67) were top-performing for predicting nodules malignancy. In the multivariate analysis of the training dataset, the AUC of all combinations of feature selection algorithms and classifiers was 0.99, and the highest sensitivity was for XGBoost classifier and MRMR feature selection algorithms (0.99). Finally, the test dataset was used to evaluate our model in which XGBoost classifier with MRMR and LASSO feature selection algorithms had the highest performance (AUC = 0.95). Ultrasound-extracted features can be used as non-invasive biomarkers for thyroid nodules' malignancy prediction.

Statistical Analysis on Impact of Image Preprocessing of CT Texture Patterns and Its CT Radiomic Feature Stability: A Phantom Study.

To examine computed tomography (CT) radiomic feature stability on various texture patterns during pre-processing utilizing the Credence Cartridge Radiomics (CCR) phantom textures. Imaging Biomarker Explorer (IBEX) expansion for the abbreviation IBEX extracted 51 radiomic features of 4 categories from 11 textures image regions of interest (ROI) of the phantom. 19 software pre-processing algorithms processed each CCR phantom ROI. All ROI texture processed image features were retrieved. Pre-processed CT image radiomic features were compared to non-processed features to measure its textural influence. Wilcoxon T-tests measured the pre-processing relevance of CT radiomic features on various textures. Hierarchical cluster analysis (HCA) was performed to cluster processer potency and texture impression likeness. The pre-processing filter, CT texture Cartridge, and feature category affect the CCR phantom CT image's radiomic properties. Pre-processing is statistically unaltered by Gray Level Run Length Matrix (GLRLM ) expansion for the abbreviation GLRLM and Neighborhood Intensity Difference matrix (NID) expansion for the abbreviation NID feature categories. The 30%, 40%, and 50% honeycomb are regular directional textures and smooth 3D-printed plaster resin, most of the image pre-processing feature alterations exhibited significant p-values in the histogram feature category. The Laplacian Filter, Log Filter, Resample, and Bit Depth Rescale Range pre-processing algorithms hugely influenced histogram and Gray Level Co-occurrence Matrix (GLCM) image features. We found that homogenous intensity phantom inserts, CT radiomic feature, are less sensitive to feature swaps during pre-processing than normal directed honeycomb and regular projected smooth 3D-printed plaster resin CT image textures. Because they lose fewer information during image enhancement, This feature concentration empowerment of the images also enhances texture pattern recognition.

Development of a new approach for rapid identification and classification of uranium ore concentrate powders using textural and spectroscopy signatures

Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates.Scanning electron microscopy images of the UOC samples were collected at 100x, 250x, 500x/1000× magnifications. A total of 524 texture features describing sample morphology were extracted from each image using First Order Statistics (FOS), Grey Level Co-Occurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), Grey Level Size Zone Matrix (GLSZM), Locally Binary Pattern (LBP) and Angle Measure Technique (AMT) textural feature extraction algorithms. Hyperspectral images of the samples were collected in the wavelength range 900–1700 nm and the mean spectrum of each sample was calculated, giving the spectral features of the UOCs. The texture and spectral features were used as input to the machine learning approach which consisted of a feature reduction step to identify discriminatory features and a classification step to provide the texture- and spectroscopy-based models. An average prediction accuracy of 82% and 97% were obtained for the texture- and spectral-based classification models, respectively, on hold-out test sets. The feature reduction step identified two new nuclear forensics signatures, consisting of 3–7 texture (morphological) characteristics and 2–6 characteristic wavelengths, respectively.This new approach enables rapid classification of unknown uranium ore concentrate powders using only their textural and hyperspectral images. By combining the classification results from the two models, the most probable production process and chemical composition can be identified, and the provenience of the seized sample can be narrowed down to a few matching possibilities. Moreover, after the image acquisition, the models can provide results in less than 2 min speeding up the investigation procedure, a key factor when a nuclear security event occurs.

Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization.

MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor prognosis. Accurate quantification is essential for risk assessment, which guides clinical decision making and disease management. This study proposes an end-to-end deep-learning framework for automatic tumor segmentation of pediatric neuroblastomas and radiomics features-based classification of MYCN gene amplification. Data from pretreatment contrast-enhanced computed tomography scans and MYCN status from 47 cases of pediatric neuroblastomas treated at a tertiary children's hospital from 2009 to 2020 were reviewed. Automated tumor segmentation and grading pipeline includes (1) a modified U-Net for tumor segmentation; (2) extraction of radiomic textural features; (3) feature-based ComBat harmonization for removal of variabilities across scanners; (4) feature selection using 2 approaches, namely, (a) an ensemble approach and (b) stepwise forward-and-backward selection method using logistic regression classifier; and (5) radiomics features-based classification of MYCN gene amplification using machine learning classifiers. Median train/test Dice score for modified U-Net was 0.728/0.680. The top 3 features from the ensemble approach were neighborhood gray-tone difference matrix (NGTDM) busyness, NGTDM strength, and gray-level run-length matrix (GLRLM) low gray-level run emphasis, whereas those from the stepwise approach were GLRLM low gray-level run emphasis, GLRLM high gray-level run emphasis, and NGTDM coarseness. The top-performing tumor classification algorithm achieved a weighted F1 score of 97%, an area under the receiver operating characteristic curve of 96.9%, an accuracy of 96.97%, and a negative predictive value of 100%. Harmonization-based tumor classification improved the accuracy by 2% to 3% for all classifiers. The proposed end-to-end framework achieved high accuracy for MYCN gene amplification status classification.