Run Length Matrix Research Articles

Recognition of Corrosion State of Water Pipe Inner Wall Based on SMA-SVM under RF Feature Selection

To solve the problem of low detection accuracy of water supply pipeline internal wall damage, a random forest algorithm with simplified features and a slime mold optimization support vector machine detection method was proposed. Firstly, the color statistical characteristics, gray level co-occurrence matrix, and gray level run length matrix features of the pipeline image are extracted for multi-feature fusion. The contribution of the fused features is analyzed using the feature simplified random forest algorithm, and the feature set with the strongest feature expression ability is selected for classification and recognition. The global search ability of the slime mold optimization algorithm is used to find the optimal kernel function parameters and penalty factors of the support vector machine model. Finally, the optimal parameters are applied to support the vector machine model for classification prediction. The experimental results show that the recognition accuracy of the classification model proposed in this paper reaches 94.710% on the data sets of different corrosion forms on the inner wall of the pipeline. Compared with the traditional Support Vector Machines (SVM) classification model, the SVM model based on differential pollination optimization, the SVM model based on particle swarm optimization, and the back propagation (BP) neural network classification model, it is improved by 4.786%, 3.023%, 4.030%, and 0.503% respectively.

Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier

The most vital organs in the human body are the brain, heart, and lungs. Because the brain controls and coordinates the operations of all other organs, normal brain function is vital. Brain tumour is a mass of tissues which interrupts the normal functioning of the brain, if left untreated will lead to the death of the subject. The classification of multiclass brain tumours using spatial fuzzy based level sets and artificial neural network (ANN) techniques is proposed in this paper. In the proposed method, images are preprocessed using Median Filtering technique, the boundaries of the Brain Tumor are obtained using Spatial Fuzzy based Level Set method, features are extracted using Gabor Wavelet and Gray-Level Run Length Matrix (GLRLM) methods. Finally ANN technique is used for the classification of the image into Normal or Benign Tumor or Malignant Tumor. The proposed method was implemented in the MATLAB working platform and achieved classification accuracy of 94%, which is significant compared to state-of-the-art classification techniques. Thus, the proposed method assist in differentiating between benign and malignant brain tumours, enabling doctors to provide adequate treatment.

Correlation of image textures of a polarization feature parameter and the microstructures of liver fibrosis tissues

Mueller matrix imaging is emerging for the quantitative characterization of pathological microstructures and is especially sensitive to fibrous structures. Liver fibrosis is a characteristic of many types of chronic liver diseases. The clinical diagnosis of liver fibrosis requires time-consuming multiple staining processes that specifically target on fibrous structures. The staining proficiency of technicians and the subjective visualization of pathologists may bring inconsistency to clinical diagnosis. Mueller matrix imaging can reduce the multiple staining processes and provide quantitative diagnostic indicators to characterize liver fibrosis tissues. In this study, a fiber-sensitive polarization feature parameter (PFP) was derived through the forward sequential feature selection (SFS) and linear discriminant analysis (LDA) to target on the identification of fibrous structures. Then, the Pearson correlation coefficients and the statistical T-tests between the fiber-sensitive PFP image textures and the liver fibrosis tissues were calculated. The results show the gray level run length matrix (GLRLM)-based run entropy that measures the heterogeneity of the PFP image was most correlated to the changes of liver fibrosis tissues at four stages with a Pearson correlation of 0.6919. The results also indicate the highest Pearson correlation of 0.9996 was achieved through the linear regression predictions of the combination of the PFP image textures. This study demonstrates the potential of deriving a fiber-sensitive PFP to reduce the multiple staining process and provide textures-based quantitative diagnostic indicators for the staging of liver fibrosis.

Exploratory analysis of radiomic as prognostic biomarkers in 18F-FDG PET/CT scan in uterine cervical cancer.

To evaluate the performance of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET/CT) radiomic features to predict overall survival (OS) in patients with locally advanced uterine cervical carcinoma. Longitudinal and retrospective study that evaluated 50 patients with cervical epidermoid carcinoma (clinical stage IB2 to IVA according to FIGO). Segmentation of the 18F-FDG PET/CT tumors was performed using the LIFEx software, generating the radiomic features. We used the Mann-Whitney test to select radiomic features associated with the clinical outcome (death), excluding the features highly correlated with each other with Spearman correlation. Subsequently, ROC curves and a Kaplan-Meier analysis were performed. A p-value < 0.05 were considered significant. The median follow-up was 23.5 months and longer than 24 months in all surviving patients. Independent predictors for OS were found-SUVpeak with an AUC of 0.74, sensitivity of 77.8%, and specificity of 72.7% (p = 0.006); and the textural feature gray-level run-length matrix GLRLM_LRLGE, with AUC of 0.74, sensitivity of 72.2%, and specificity of 81.8% (p = 0.005). When we used the derived cut-off points from these ROC curves (12.76 for SUVpeak and 0.001 for GLRLM_LRLGE) in a Kaplan-Meier analysis, we can see two different groups (one with an overall survival probability of approximately 90% and the other with 30%). These biomarkers are independent of FIGO staging. By radiomic 18F-FDG PET/CT data analysis, SUVpeak and GLRLM_LRLGE textural feature presented the best performance to predict OS in patients with cervical cancer undergoing chemo-radiotherapy and brachytherapy.

Radiomics in bone pathology of the jaws.

To define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response. A comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Agnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results. GLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists' reports, decreasing the subjectivity and leading to personalized healthcare.

Combining Radiomics Features with Immune Response Biomarkers to Build a XGBoost Model to Predict Radiation Pneumonitis (RP) in Patients with Primary Lung Cancer

<h3>Purpose/Objective(s)</h3> Patients are heterogenous in their responses to radiation lung damage. It has been reported mostly from western people that the computed tomography (CT) radiomics features have a potential to identify patients at high risk for radiation pneumonitis (RP) in Western people. The purpose of this study was to 1) validate the significance of radiomics features on RP prediction, 2) exam the differences in systemic level of immune checkpoint indoleamine 2,3-dioxygenase (IDO) in patients with RP and without RP, and 3) explore the performance of the extreme gradient boosting (XGBoost) of combining above factors, on grade 2 and above RP in Chinese patients with primary lung cancer. <h3>Materials/Methods</h3> Planning CT scans and blood of baseline and end of treatment from 43 patients treated for primary lung cancer with radiotherapy were collected. Grade 2 and above RP was defined as cough or short of breath need medication treatment during or at the end of radiotherapy. Radiomics features were extracted from lung-GTV volume in planning CT using python package open-source software. Serum kynurenine, tryptophan and kynurenine: tryptophan ratio, which is IDO systemic activity related biomarkers (IDO biomarker) were measured at pre-RT and end of RT. The relation between features [radiomics features and IDO biomarker] and RP. Finally, the radiomics features with p value smaller than 0.1 were used for modeling. Patients were randomly split into 80% for training and 20% for validation. Model was built with XGBoost in train dataset and was tested in independent test dataset. The model predictive ability was assessed using area under the receiver operating characteristic curve (AUC). <h3>Results</h3> Seven out of 43 (16.3%) patients presented grade 2 and above RP. A total of 109 radiomics features were extracted. A total of 31 features, including 6 first-order, 4 gray level co-occurrence matrix (GLCM), 5 gray level dependence matrix (GLDM), 5 gray level run length matrix (GLRLM), 3 gray level size zone matrix (GLSZM) and 8 shape features, were significantly different (p value ≤ 0.05) between patients with RP and without RP. The IDO biomarkers at pre-RT and end of RT seemed to be non-significant (p value: 0.46-0.81). 43 radiomics features with p values smaller than 0.1 were used for model building. AUC of in the training dataset was 0.86 [95% CI 0.75-1] and of test dataset was 0.75 [95% 0.5-1]. A model of combined IDO biomarkers and radiomics features to build model, The predictive AUC of the training dataset was 0.9 [0.75-1] and of test dataset was 0.75 [0.5-1]. AUC slightly improved on training set. <h3>Conclusion</h3> This study at some degree validated the significance of radiomics features extracted from planning CT on predicting grade 2 and above RP in primary lung cancer in Chinese patients which has not been reported previously. IDO biomarkers did seem help, but the model built with XGBoost approach improved the predictive ability. Study with larger number of patients and ideally from multicenters are needed to validate this finding.

Local Homogeneity of PET Images Visualizes Peri-Irradiation CD8 T Cell Exhaustion

<h3>Purpose/Objective(s)</h3> The functional states of CD8⁺ T cells are associated with response to immune checkpoint inhibitors (ICI) and might become a potential biomarker indicating the timing of ICI. CD8⁺ T cells undergoing exhaustion (Tex) exhibit progressive loss of effector functions as well as metabolic insufficiency, such as suppressed glycolysis and restricted glucose uptake. We assumed that metabolic characteristics of Tex might be reflected by PET radiomic features, which could be used to noninvasively visualize the peri-irradiation dynamics of Tex and predict the optimal timing of ICI integration. Here, we aim to investigate the dynamics of Tex after ablative irradiation and develop an ¹⁸F-FDG PET/CT radiomic approach to assess tumor infiltrating Tex basing on non-small cell lung cancer (NSCLC) xenograft experiments. <h3>Materials/Methods</h3> C57BL/6 mice were inoculated with 1M Lewis Lung Carcinoma (LLC) cells subcutaneously on the right flank. On day 10 post-tumor inoculation, 20Gy was delivered to the tumors in a single fraction. For the control (D0) and those irradiated (D3, D7, D14 and D21 post-irradiation), mice received micro PET-CT scans and were sacrificed immediately after scanning, with tumors and spleens collected. The abundance of tumor-infiltrating immune cell subsets were estimated by deconvoluting the tumor microarray data with CIBERSORTx. ICI-responsive early Tex (CD8⁺PD-1^intTcf-1⁺) and ICI-nonresponsive terminal Tex (CD8⁺PD-1^hiTIM-3⁺TOX⁺) were identified by flow cytometry. Tumors on PET/CT images were manually contoured, and radiomic features were extracted by a compact proton therapy machine. Robust features which passed Test-retest analysis will undergo further selection. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to select radiomic features significantly correlated with the infiltration of early or terminal Tex. <h3>Results</h3> After ablative irradiation, the tumor CD8⁺ T cell infiltration increased from D3 through the peak on D14, and then declined sharply to a below-baseline level on D21. However, dysfunctional terminal Tex significantly predominated CD8⁺ T cells from D7 to D14 (69.44%∼ 76.35%), indicating a potential window for ICI treatment between D3-D7 post-irradiation. 40 (out of 57) CT and 9 (out of 61) PET radiomic features of good robustness were brought into LASSO regression. Gray Level Co-occurrence Matrix (GLCM)-inverse variance and Gray Level Run Length Matrix (GLRLM)-long run emphasis on PET images were found to be inversely correlated with the abundance of terminal Tex, showing more heterogeneous local distribution of FDG uptake correlated with higher T exhaustion. <h3>Conclusion</h3> The dynamics of Tex help identify the proper timing to combine ICI with irradiation. Local homogeneity of PET images may provide a noninvasive approach to visualize peri-irradiation T cell exhaustion, and exhibit the potential to estimate ICI timing among more radiation dose-fractionation schedules in future study.

Leveraging Serial MRI Radiomics and Machine Learning to Predict Risk of Radiation Necrosis in Patients with Brain Metastases Managed with Stereotactic Radiation and Immunotherapy

<h3>Purpose/Objective(s)</h3> Radiation necrosis (RN) is the primary dose-limiting toxicity of stereotactic radiotherapy (SRT) for brain metastases (BMs), especially in patients also receiving immunotherapy (IO). We investigated the utility of serial magnetic resonance imaging (MRI) radiomic features to develop predictive quantitative imaging biomarkers of brain metastases response to SRT and IO. <h3>Materials/Methods</h3> Using an IRB-approved single-institution database, we retrospectively identified adult patients with brain metastases managed with SRT and IO between 2006 and 2021. We collected per-lesion outcomes measures of RN (radiographic and/or symptomatic), progressive disease (PD), or neither (NA). Routine pre- and post-SRT contrast-enhanced T1-weighted MRIs were pushed to MIM Software (Beachwood, OH), where individual BMs were manually contoured. A total of 1061 radiomics features were extracted per lesion using a digital gene expression panel. They were selected from the following categories: shape, intensity, neighborhood intensity difference (NID), grey-level co-occurrence matrix (GLCM), and grey-level run-length matrix (GLRLM). Feature reduction followed using Spearman's correlation coefficient (0.3 cut-off). Features with non-significantly different mean ranks pre- and post-SRT (p<0.01) per Wilcoxon signed-rank test were also excluded. Simple then multiple nominal logistic regressions (SLR and MLR) with Bonferroni correction were used to model the risk of RN, PD, or NA based on pre- and post-SRT, and delta-radiomic ([post-pre]/pre) features. 95% CI of ROC AUC was obtained by 10,000 bootstrapping. Statistical analysis was performed using software. <h3>Results</h3> Our dataset encompassed 301 BMs from 92 patients with NSCLC, melanoma, and renal cell carcinoma, all treated with brain SRT and IO. 74 (24.6%), 75 (24.9%), and 152 developed RN, PD, or NA, respectively. The final radiomic feature set included 39 features that significantly changed post-SRT (p<0.01). MLR resulted in a 3-radiomic feature model that predicted risk of RN (ROC AUC 0.71). The model included post-SRT BM surface area and texture (GLCM 3D Homogeneity) and pre-RT BM roundness. Interestingly, another 5-radiomic feature MLR model predicted RN and PD (ROC AUC 0.71) based on pre-SRT GLCM 3D texture feature, 3 post-SRT shape features (surface area, mean breadth, and roundness), in addition to delta-volume. <h3>Conclusion</h3> Our analysis showed that static and serial MR radiomic features could potentially predict BMs response to SRT and IO. Model external validation, combination with clinical variables, and integration of other MRI sequences are warranted before the large-scale adoption of this novel diagnostic approach.

Utilization of Radiomics to Predict Radiation Associated Cardiovascular Morbidity in Lung SBRT

<h3>Purpose/Objective(s)</h3> SBRT for early lung cancers is now considered standard of care for some patients, but there is increasing evidence linking long-term cardiac toxicity with cardiovascular dose. While the dose constraints for aortic radiation exposure are still unclear, there is accumulating data that higher aortic doses can be associated with mortality. Our study explores the link between central thoracic radiation and early radiomic changes, determining if these radiomic changes are associated with relevant cardiovascular toxicity. This information will allow us to identify the most relevant dosing information for a variety of central thoracic organs in predicting cardiovascular toxicity from SBRT to lung. <h3>Materials/Methods</h3> We analyzed 27 patients who received SBRT to lung tumors with curative intent. Pretreatment and post treatment contrast enhanced diagnostic CT scans were accessed. The following structures were manually contoured by a medical student and reviewed by an MD: aorta, vena cava, pulmonary artery, heart, L atrium, R atrium, L ventricle, R Ventricle, and septum. For this analysis, the aorta was divided in to 10 equal sections along the inferior/superior direction. 1) 168 radiomic features were extracted for each section of the aorta wall for all 27 patients. (Mean value /standard deviation >10) between patients was used as a criterion to determine the useful and stable features. 2) dose dependent aortic wall volumes for 26 pre and post (up to 4.5 years post) treatment contrast enhanced CT scans were evaluated for the radiomic feature changes with dose. <h3>Results</h3> 1) Only 13 radiomic features were deemed stable according to our radiomic feature analysis between patients across all 10 sections of the aortic wall. These include 4 Gray-Level Co-occurrence Matrix (GLCM) features, 1 Gray Level Dependence Matrix (GLDM) feature, 5 Gray-Level Run-Length Matrix (GLRLM) features, and 3 Gray Level Size Zone Matrix (GLSZM) feature. 2) Early results indicate that no statistically significant radiomic changes of these 13 parameters were observed post SBRT to lung up to 4.5 years even with a dose of 55 Gy to the aorta. These observations were confirmed by the patient's clinical status. <h3>Conclusion</h3> Preliminary analysis suggests that radiomic features for contrast enhanced CTs could be used to evaluate the absence of radiation damage to cardiovascular structures.

An efficient classification of cirrhosis liver disease using hybrid convolutional neural network-capsule network

Liver cirrhosis is the diffuse and advanced phase of liver disease. Several morphological methods are used for imaging modalities. But, these modalities are biased and lack in higher detection accuracy. Hence, this work introduces automated cirrhosis liver disease classification using an optimized hybrid deep learning model. In this work, Magnetic Resonance Image (MRI) is considered for the process. Initially, an Extended Guided Filter (EGF) is used for eliminating the noise from input MRI images. Binomial thresholding is used to segment the tumor from the image. Then, Feature Extraction (FE) phase is carried out by Grey Level Co-occurrence Matrix (GLCM) and Gray level Run-length Matrix (GRLM). Finally, a hybrid of two Deep Learning (DL) algorithms Convolutional Neural Network and Capsule Network (HCNN-CN) are integrated to classify the Cirrhosis liver disease. Moreover, for fine tuning the parameters of the neural network, an optimization approach Adaptive Emperor Penguin Optimization (AEPO) is used. The proposed HCNN-CN-AEPO is compared over several approaches and depicted accuracy and sensitivity value of 0.993 and 0.986 on the real time dataset. The experimental results proved that the proposed HCNN-CN-AEPO can exactly diagnose the tumour.

Machine Learning Model to Predict Diagnosis of Mild Cognitive Impairment by Using Radiomic and Amyloid Brain PET.

The study aimed to develop a deep learning model for predicting amnestic mild cognitive impairment (aMCI) diagnosis using radiomic features and amyloid brain PET. Subjects (n = 328) from the Alzheimer's Disease Neuroimaging Initiative database and the EudraCT 2015-001184-39 trial (159 males, 169 females), with a mean age of 72 ± 7.4 years, underwent PET/CT with 18 F-florbetaben. The study cohort consisted of normal controls (n = 149) and subjects with aMCI (n = 179). Thirteen gray-level run-length matrix radiomic features and amyloid loads were extracted from 27 cortical brain areas. The least absolute shrinkage and selection operator regression was used to select features with the highest predictive value. A feed-forward neural multilayer network was trained, validated, and tested on 70%, 15%, and 15% of the sample, respectively. Accuracy, precision, F1-score, and area under the curve were used to assess model performance. SUV performance in predicting the diagnosis of aMCI was also assessed and compared with that obtained from the machine learning model. The machine learning model achieved an area under the receiver operating characteristic curve of 90% (95% confidence interval, 89.4-90.4) on the test set, with 80% and 78% for accuracy and F1-score, respectively. The deep learning model outperformed SUV performance (area under the curve, 71%; 95% confidence interval, 69.7-71.4; 57% accuracy, 48% F1-score). Using radiomic and amyloid PET load, the machine learning model identified MCI subjects with 84% specificity at 81% sensitivity. These findings show that a deep learning algorithm based on radiomic data and amyloid load obtained from brain PET images improves the prediction of MCI diagnosis compared with SUV alone.

Machine learning improves early detection of liver fibrosis by quantitative ultrasound radiomics.

Progression of liver fibrosis to cirrhosis, a severe non-reversible process, is one of the most critical risk factors in developing hepatocellular carcinoma and liver failure. Detection of liver fibrosis at an early stage is therefore essential for better patient management. Ultrasound (US) imaging can provide a noninvasive alternative to biopsies. This study evaluates quantitative US texture features to improve early-stage versus advanced liver fibrosis detection. 157 B-mode US images of different liver lobes acquired from early and advanced fibrosis rat cases were used for analysis. 5-6 regions of interest were placed on each image. Twelve quantitative features that describe liver texture changes were extracted from the images, including first-order histogram, run length (RL), and gray level co-occurrence matrix (GLCM). The diagnostic performance of individual features was high with AUC ranging from 0.80 to 0.94. Logistic regression with leave-one-out cross-validation was used to evaluate the performance of the combined features. All features combined showed a slight improvement in performance with AUC = 0.95, sensitivity = 96.8%, and specificity = 93.7%. Quantitative US texture features characterize liver fibrosis changes with high accuracy and can differentiate early from advanced disease. Quantitative ultrasound, if validated in future clinical studies, can have a potential role in identifying fibrosis changes that are not easily detected by visual US image assessments.

A Novel System for Precise Grading of Glioma.

Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Accurate grading of gliomas is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive non-invasive multimodal magnetic resonance (MR)-based computer-aided diagnostic (CAD) system to precisely differentiate between different grades of gliomas (Grades: I, II, III, and IV). A total of 99 patients with gliomas (M = 49, F = 50, age range = 1–79 years) were included after providing their informed consent to participate in this study. The proposed imaging-based glioma grading (GG-CAD) system utilizes three different MR imaging modalities, namely; contrast-enhanced T1-MR, T2-MR known as fluid-attenuated inversion-recovery (FLAIR), and diffusion-weighted (DW-MR) to extract the following imaging features: (i) morphological features based on constructing the histogram of oriented gradients (HOG) and estimating the glioma volume, (ii) first and second orders textural features by constructing histogram, gray-level run length matrix (GLRLM), and gray-level co-occurrence matrix (GLCM), (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and contrast-enhancement slope. These features are then integrated together and processed using a Gini impurity-based selection approach to find the optimal set of significant features. The reduced significant features are then fed to a multi-layer perceptron artificial neural networks (MLP-ANN) classification model to obtain the final diagnosis of a glioma tumor as Grade I, II, III, or IV. The GG-CAD system was evaluated on the enrolled 99 gliomas (Grade I = 13, Grade II = 22, Grade III = 22, and Grade IV = 42) using a leave-one-subject-out (LOSO) and k-fold stratified (with k = 5 and 10) cross-validation approach. The GG-CAD achieved 0.96 ± 0.02 quadratic-weighted Cohen’s kappa and 95.8% ± 1.9% overall diagnostic accuracy at LOSO and an outstanding diagnostic performance at k = 10 and 5. Alternative classifiers, including RFs and SVM produced inferior results compared to the proposed MLP-ANN GG-CAD system. These findings demonstrate the feasibility of the proposed CAD system as a novel tool to objectively characterize gliomas using the comprehensive extracted and selected imaging features. The developed GG-CAD system holds promise to be used as a non-invasive diagnostic tool for Precise Grading of Glioma.

Texture analysis of sonographic image of placenta in pregnancies with normal and adverse outcomes, a pilot study

IntroductionMany maternal and fetal morbidity and mortality from complications of pregnancy have been attributed to placenta abnormality. Placenta assessment in developing countries is mainly through ultrasonography which is subjective and prone to error. Objective assessment of placental abnormalities through texture analysis has been frequently done using magnetic resonance images with little done on ultrasound generated images, thus, the need for this study. The study is aimed at using statistical texture analysis in characterizing placenta tissue into normal and abnormal placenta as well as testing the accuracy of different texture analysis algorithms in differentiating placenta into normal and abnormal placental tissues. MethodsThis longitudinal study involved 500 ultrasound-generated placenta images from patients screened for adverse pregnancy outcomes in a private hospital in Enugu. These images were loaded onto an HP laptop for viewing. Two regions of interest were selected from the placenta tissue where texture features were extracted and were classified into normal and abnormal placentas using MaZda® software version 47 while the accuracy of the classification descriptors was assessed using WEKA classification algorithms. ResultsCo-occurrence matrix, run length matrix and histogram parameters differentiated normal placenta tissue from abnormal placental tissues (p-value <0.05) while variance is the only absolute gradient parameter that can differentiate normal placenta tissue from abnormal placenta tissues. All feature descriptors show high classification accuracy using KNN and ANN algorithms. ConclusionTexture analysis can differentiate normal placenta tissues from abnormal placenta tissue which will reduce the errors associated with subjective assessment of the placenta echogenicity. Implications for practiceIntegrating these computer-aided algorithms into our ultrasound machines will lead to early detection of abnormal placenta tissues as early management results in better pregnancy outcomes.

Relationship of Textures from Tomato Fruit Images Acquired Using a Digital Camera and Lycopene Content Determined by High-Performance Liquid Chromatography

This study aimed at correlating image features with the lycopene content of tomato fruit. Tomato cultivars with different fruit colors, such as ‘Ożarowski’ (yellow), ‘Marvel Striped’ (yellow-orange-pink), ‘Green Zebra’ (green), Sandoline F1 (red), Cupidissimo F1 (red), and Sacher F1 (brown) were selected for the study. The tomato fruits were imaged using a digital camera. The texture parameters were computed from the images converted to color channels R, G, B, L, a, b, X, Y, and Z based on the histogram, autoregressive model, gradient map, co-occurrence matrix, and run-length matrix. Lycopene content was determined using high-performance liquid chromatography (HPLC). Pearson’s correlation coefficients (R), regression equations, and coefficients of determination (R2) were determined. The lycopene content in fruit ranged from 0.31 mg 100 g−1 for ‘Green Zebra’ to 11.83 mg 100 g−1 for Sacher F1. The correlation coefficient (R) between lycopene content and selected image textures reached −0.99 for selected textures from color channels G, b, and Y. The highest positive correlation (R parameter equal to 0.98) was obtained for texture from color channel Y. Based on the individual color channel providing the highest results, one texture was selected for the determination of regression equations. Coefficients of determination (R2) of 0.99 were obtained for texture from color channel G. The regression equations may be used in practice for nondestructive, objective, and precise estimation of the lycopene content in tomato fruit.

Non-contrast-enhanced CT texture analysis of primary and metastatic pancreatic ductal adenocarcinomas: value in assessment of histopathological grade and differences between primary and metastatic lesions

PurposeTo evaluate the utility of non-contrast-enhanced CT texture analysis (CTTA) for predicting the histopathological differentiation of pancreatic ductal adenocarcinomas (PDAC) and to compare non-contrast-enhanced CTTA texture features between primary PDAC and hepatic metastases of PDAC.MethodsThis retrospective study included 120 patients with histopathologically confirmed PDAC. Sixty-five patients underwent CT-guided biopsy of primary PDAC, while 55 patients underwent CT-guided biopsy of hepatic PDAC metastasis. All lesions were segmented in non-contrast-enhanced CT scans for CTTA based on histogram analysis, co-occurrence matrix, and run-length matrix. Statistical analysis was conducted for 372 texture features using Mann–Whitney U test, Bonferroni–Holm correction, and receiver operating characteristic (ROC) analysis. A p value < 0.05 was considered statistically significant.ResultsThree features were identified that differed significantly between histopathological G2 and G3 primary tumors. Of these, “low gray-level zone emphasis” yielded the largest AUC (0.87 ± 0.04), reaching a sensitivity and specificity of 0.76 and 0.83, respectively, when a cut-off value of 0.482 was applied. Fifty-four features differed significantly between primary and hepatic metastatic PDAC.ConclusionNon-contrast-enhanced CTTA of PDAC identified differences in texture features between primary G2 and G3 tumors that could be used for non-invasive tumor assessment. Extensive differences between the features of primary and metastatic PDAC on CTTA suggest differences in tumor microenvironment.Graphical

Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging.

Before implementing radiomics in routine clinical practice, comprehensive knowledge about the repeatability and reproducibility of radiomic features is required. The aim of this study was to systematically investigate the influence of image processing parameters on radiomic features from magnetic resonance imaging (MRI) in terms of feature values as well as test-retest repeatability. Utilizing a phantom consisting of 4 onions, 4 limes, 4 kiwifruits, and 4 apples, we acquired a test-retest dataset featuring 3 of the most commonly used MRI sequences on a 3 T scanner, namely, a T1-weighted, a T2-weighted, and a fluid-attenuated inversion recovery sequence, each at high and low resolution. After semiautomatic image segmentation, image processing with systematic variation of image processing parameters was performed, including spatial resampling, intensity discretization, and intensity rescaling. For each respective image processing setting, a total of 45 radiomic features were extracted, corresponding to the following 7 matrices/feature classes: conventional indices, histogram matrix, shape matrix, gray-level zone length matrix, gray-level run length matrix, neighboring gray-level dependence matrix, and gray-level cooccurrence matrix. Systematic differences of individual features between different resampling steps were assessed using 1-way analysis of variance with Tukey-type post hoc comparisons to adjust for multiple testing. Test-retest repeatability of radiomic features was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient. Image processing influenced radiological feature values. Regardless of the acquired sequence and feature class, significant differences ( P < 0.05) in feature values were found when the size of the resampled voxels was too large, that is, bigger than 3 mm. Almost all higher-order features depended strongly on intensity discretization. The effects of intensity rescaling were negligible except for some features derived from T1-weighted sequences. For all sequences, the percentage of repeatable features (concordance correlation coefficient and dynamic range ≥ 0.9) varied considerably depending on the image processing settings. The optimal image processing setting to achieve the highest percentage of stable features varied per sequence. Irrespective of image processing, the fluid-attenuated inversion recovery sequence in high-resolution overall yielded the highest number of stable features in comparison with the other sequences (89% vs 64%-78% for the respective optimal image processing settings). Across all sequences, the most repeatable features were generally obtained for a spatial resampling close to the originally acquired voxel size and an intensity discretization to at least 32 bins. Variation of image processing parameters has a significant impact on the values of radiomic features as well as their repeatability. Furthermore, the optimal image processing parameters differ for each MRI sequence. Therefore, it is recommended that these processing parameters be determined in corresponding test-retest scans before clinical application. Extensive repeatability, reproducibility, and validation studies as well as standardization are required before quantitative image analysis and radiomics can be reliably translated into routine clinical care.

Evaluation of MRI-based radiomic features in heart morphologic variations as a consequence of autoimmune thyroid disorders.

Radiomics (RC) was initially developed using computed tomography (CT) for oncological imaging. However, it can be applied to various scientific and clinical radiology fields regardless of the modalities involved. The purpose of this survey was to evaluate alterations in magnetic resonance imaging of the heart (CMR) in patients suffering from autoimmune thyroid disorders (AITD) by applying RC tools and mapping features. A total of 50 individuals were evaluated in this study. We searched for CMR examinations performed in our department between January 2019 and 2021 in patients with AITD. Thirty patients with AITD (21 men and 9 women, aged 51 to 78 years; mean age, 60 years) were enrolled in our survey. We enrolled a control group (CG) of 20 individuals (14 men and 6 women aged 53–87 years; mean age, 68 years) without AITD or cardiac disorders. Global native T1 and T2 mapping revealed no significant differences between groups. However, we identified significantly higher values of several texture parameters, including the gray-level co-occurrence matrix (GLCM) entropy, gray-level run-length matrix (GLRLM; short-run high gray-level emphasis (SRHGE), GLCM (Energy), gray-level size zone matrix length matrix (GLZLM; LZLGE), GLZLM (SZLGE), DISCRETIZED (HISTO-Energy) GLCM (Dissimilarity), and GLCM (Contrast), in patients with AITD in comparison to the CG (P < .01). Our results indicate that several RC properties extracted from CMR images can be used to discriminate between the AITD and CG groups.

Optimization assisted framework for thyroid detection and classification: A new ensemble technique

Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and detection of thyroid disorders is still in its infant stage. This study aims to present a new thyroid diagnosis approach, which consists of three phases like “(i) feature extraction, (ii) feature dimensionality reduction, and (iii) classification”. Initially, the thyroid images as well as its related data are given as input. From the input image, the features such as“ Grey Level Co-occurrence Matrix(GLCM), Grey level Run Length Matrix(GLRM), proposed Local Binary Pattern(LBP), and Local Tetra Patterns (LTrP)” are extracted. Meanwhile, from the input data, the higher-order statistical features like skewness, kurtosis, entropy, as well as moment get retrieved. Consequently, the Linear Discriminant Analysis (LDA) based dimensionality reduction is processed to resolve the problem of “curse of dimensionality”. Finally, the classification is carried out via two phases: Image features are classified using an ensemble classifier that includes Support Vector Machine (SVM)& Neural Network(NN) models. The data features are subjected to Recurrent Neural Network(RNN) based classification, which is optimized by an Adaptive Elephant Herding Algorithm (AEHO) via tuning the optimal weight. At last, the performance of the adopted scheme is compared to the extant models in terms of various measures. Especially, the mean value of the suggested RNN + AEHO model is 4.35%, 3.54%, 6.07%, 3.8%, 1.69%, 2.85%, 2.07%, 2.54%, 0.13%, 0.035%, and 8.53% better than the existing CNN, NB, RF, KNN, Levenberg, RNN + EHO, RNN + FF, RNN + WOA, WF-CS, FU-SLnO and HFBO methods respectively.

Multiparametric MR radiomics in brain glioma: models comparation to predict biomarker status

BackgroundGenotype status of glioma have important significance to clinical treatment and prognosis. At present, there are few studies on the prediction of multiple genotype status in glioma by method of multi-sequence radiomics. The purpose of the study is to compare the performance of clinical features (age, sex, WHO grade, MRI morphological features etc.), radiomics features from multi MR sequence (T2WI, T1WI, DWI, ADC, CE-MRI (contrast enhancement)), and a combined multiple features model in predicting biomarker status (IDH, MGMT, TERT, 1p/19q of glioma.MethodsIn this retrospective analysis, 81 glioma patients confirmed by histology were enrolled in this study. Five MRI sequences were used for radiomic feature extraction. Finally, 107 features were extracted from each sequence on Pyradiomics software, separately. These included 18 first-order metrics, such as the mean, standard deviation, skewness, and kurtosis etc., 14 shape features and second-order metrics including 24 grey level run length matrix (GLCM), 16 grey level run length matrix (GLRLM), 16 grey level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix (NGTDM), and 14 grey level dependence matrix (GLDM). Then, Univariate analysis and LASSO (Least absolute shrinkage and selection operator regression model were used to data dimension reduction, feature selection, and radiomics signature building. Significant features (p < 0.05 by multivariate logistic regression were retained to establish clinical model, T1WI model, T2WI model, T1 + C (T1WI contrast enhancement model, DWI model and ADC model, multi sequence model. Clinical features were combined with multi sequence model to establish a combined model. The predictive performance was validated by receiver operating characteristic curve (ROC analysis and decision curve analysis (DCA).ResultsThe combined model showed the better performance in some groups of genotype status among some models (IDH AUC = 0.93, MGMT AUC = 0.88, TERT AUC = 0.76). Multi sequence model performed better than single sequence model in IDH, MGMT, TERT. There was no significant difference among the models in predicting 1p/19q status. Decision curve analysis showed combined model has higher clinical benefit than multi sequence model.ConclusionMulti sequence model is an effective method to identify the genotype status of cerebral glioma. Combined with clinical models can better distinguish genotype status of glioma.Key PointsThe combined model showed the higher performance compare with other models in predicting genotype status of IDH, MGMT, TERT.Multi sequence model showed a better predictive model than that of a single sequence model.Compared with other models, the combined model and multi sequence model show no advantage in prediction of 1p/19q status.