GLCM Features Research Articles

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.

Abstract A040: CT Radiomics to predict early recurrence of margin-negative resectable pancreatic ductal adenocarcinoma

Abstract Radiographic CT/MRI staging is pivotal to accurate therapeutic decision-making, and guides the surgical resectability of pancreatic ductal adenocarcinoma (PDAC). The extent of vascular involvement of the lesion as seen through radiographic imaging, which differentiates the regional versus locally advanced disease, and therefore upfront resectability. Following standard criteria, only about 20% of PDAC patients qualify for an upfront surgery but half of them recur within a year, despite the complete removal (margin-negative R0 resection) of the primary tumor It underscores the necessity for a better deciphering of the CT/MRI-based clinical imaging of PDAC using radiomics—the process of extracting high dimensional quantitative imaging information from radiographic images. We have previously described a prospective cohort of resected PDAC (n=161) at Memorial Sloan Kettering Cancer Center for preoperative CT angiography collected between 2009 and 2012. We demonstrated that CT texture features are associated with overall survival. Utilizing the same cohort of resected PDAC, the current study aims to develop a much deeper case-controlled algorithm to further identify the texture features are predictive of early recurrence for margin-negative PDAC patients. We selected all patients following these criteria: margin negative (R0), Gemcitabine/nab-paclitaxel adjuvant chemotherapy (excluding neoadjuvant), clinical-stage IIB, moderate tumor differentiation by histopathology, and no lymph node and distant metastasis. Based on these criteria, a total of 27 patients were selected, where 16 patients had recurred within a year of surgery (high-risk group) and the remaining 11 patients did not recur in 2-years (low-risk group). We focused on Gray-level co-occurrence (GLCM) based on Haralick’s features. GLCM is most extensively used to characterize the texture of a tumor via encoding the spatial distribution of voxels—neighboring pixels of an image. A total of 18 GLCM features were extracted from manually segmented CT of PDAC primary tumors. Univariate analysis with Wilcoxon-rank-sum test (p<0.05) revealed 4 features –energy (GLCM1) and entropy (GLCM9, GLCM17, and GLCM18) based were associated with early recurrence. Energy, or angular speed moment, is a measure of local variation in an image; higher energy represents homogeneity between neighboring pixels. On the contrary, entropy characterizes randomness within an image; higher entropy represents more heterogeneity. The energy feature GLCM1 was significantly lower in high-risk group, whereas the entropy features (GLCM 9, 17, and 18) were significantly higher in high-risk group compared to the low-risk group – suggesting the relationship of tumor heterogeneity with early recurrence. While proposing these GLCM features as the measure of tumor heterogeneity and early recurrence, we are validating them on a larger independent cohort, estimating their benchmark measures of predictive ability. The long-term goal is to develop these as radiomic biomarkers for better guiding the therapeutic decision-making of PDAC. Citation Format: Anwesha Dhara, Liana Langdon-Embry, Michael I. D’Angelica, T. Peter Kingham, Natally Horvat, William R. Jarnagin, Alice C. Wei, Jayasree Chakraborty. CT Radiomics to predict early recurrence of margin-negative resectable pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A040.

Decoding Brain Fog in Head and Neck Cancer Survivors Using Artificial Intelligence

<h3>Purpose/Objective(s)</h3> Brain fogging, which is characterized by memory decline, word-finding difficulty, and decreased multi-tasking ability, is common in head and neck cancer survivors. The precise causes of brain fog are poorly understood, as MRIs are essentially normal in these patients. The purpose of this study was to use artificial intelligence (AI) to detect micro-architectural and biological changes in the brain that are invisible to human eyes. <h3>Materials/Methods</h3> DVH of temporal lobes and whole brain were obtained in 566 and 60 patients, respectively, who underwent IMRT or proton radiation with concurrent chemotherapy for nasopharyngeal carcinoma. EORTC QLQ-C30 was obtained from 92 patients who enrolled in two prospective clinical studies. Automated cortical region segmentation using an open-source software for processing and analyzing brain MRI images was performed in 33 patients with a minimum follow-up of 4 years. Architectural and biological MRI changes in gray matter, white matter, and subcortical brain regions were determined in 34 brain regions. Graph theory-based brain connectome was evaluated to assess the connectivity between different regions of the brain. Radiomics which employs high-throughput quantitative analysis of image features were performed on the T1- and diffusion-weighted sequences. <h3>Results</h3> Patients experienced a continuous decline of cognitive function after radiation, with a 6.6% in decrease in their first year. The brain received a large low-dose bath with a median V10-20(whole brain) (volume of whole brain that received 10-20 Gy) 170cc, V20-30(whole brain) 50.8cc, V10-20(temporal lobe)11.4cc, and V20-30(temporal lobe) 6.18cc. There were global changes in gray matter thickness, with most pronounced changes occurred in parietal lobe (-4.79%, p=0.007) and occipital lobe (-5.68%, p=0.03). Similarly, there were diffuse changes in white matter and subcortical volume. After radiation, the frontal lobe increased by 17.5% (p=0.04), lateral ventricle 41% (p=0.03), and choroid plexus 34.3% (p=0.03). After radiation, the brain exhibited diffuse disrupted connectivity as characterized by increased clustering coefficient (p=0.002) and decreased modularity (p=0.003). Corpus collusum, choroid plexus, cerebellum, hippocampus, brainstem, medialorbito- and superior-frontal, temporal pole, and inferior temporal were analyzed using radiomics. First order interquartile-range, entropy, uniformity, and GLCM features were found to be statistically significant (p< 0.01). After adjusted for age, gender, and time interval between MRI, IMRT patients developed significant more changes in white matter of the temporal lobe (p=0.03) and corpus callosum (p=0.03) when compared to proton patients. <h3>Conclusion</h3> By employing artificial intelligence and computational methods, we are able to detect global disruption in architecture, connectivity, and function in the brain of head and neck cancer survivors. IMRT affects the brain differently than proton.

Malignant melanoma diagnosis applying a machine learning method based on the combination of nonlinear and texture features

Skin cancer affects people of all skin tones, including those with darker complexions. Melanomas are known as malignant tumors of skin cancer, resulting in an adverse prognosis, responsible for most deaths relating to skin cancer. Early diagnosis and treatment of skin cancer from dermoscopic images can significantly reduce mortality and save lives. In this paper, Computer-Aided Diagnosis (CAD) system based on machine learning algorithms is provided to classify various skin cancer types. The proposed method uses the Online Region-based Active Contour Model (ORACM) with a new binary level set equation and regularization operation to extract skin lesions' Region of Interest (ROI). Additionally, various combinations of different textures and nonlinear features are extracted for the ROI to show the multiple aspects of skin lesions. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are applied to the combination of the extracted features in which, the Non-dominated Sorting Genetic Algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Fitting neural network (Fit net), Feed-Forward neural network (FF net), and Pattern recognition network (Pat net) are employed for the classification. In this paper, the best-obtained evaluation parameters based on fivefold cross-validation belong to the combination of selected nonlinear indices, texture, and GLCM features with an accuracy: 92.24 %, specificity: 100 %, and sensitivity: 100 %, resulting through NSGA II and applying the pattern net classifier. Besides, the comparison between this paper's experimental results and other similar works demonstrates the proposed method's efficiency.

Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach.

Background and purposeSemantic imaging features have been used for molecular subclassification of high-grade gliomas. Radiomics-based prediction of molecular subgroups has the potential to strategize and individualize therapy. Using MRI texture features, we propose to distinguish between IDH wild type and IDH mutant type high grade gliomas.MethodsBetween 2013 and 2020, 100 patients were retrospectively analyzed for the radiomics study. Immunohistochemistry of the pathological specimen was used to initially identify patients for the IDH mutant/wild phenotype and was then confirmed by Sanger’s sequencing. Image texture analysis was performed on contrast-enhanced T1 (T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on MR image slices followed by single-slice multiple sampling image augmentation. Both whole tumor multislice segmentation and single-slice multiple sampling approaches were used to arrive at the best model. Radiomic features were extracted, which included first-order features, second-order (GLCM—Grey level co-occurrence matrix), and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression, followed by radiomic classification using Support Vector Machine (SVM) and a 10-fold cross-validation strategy for model development. The area under the Receiver Operator Characteristic (ROC) curve and predictive accuracy were used as diagnostic metrics to evaluate the model to classify IDH mutant and wild-type subgroups.ResultsMultislice analysis resulted in a better model compared to the single-slice multiple-sampling approach. A total of 164 MR-based texture features were extracted, out of which LASSO regression identified 14 distinctive GLCM features for the endpoint, which were used for further model development. The best model was achieved by using combined T1C and T2W MR images using a Quadratic Support Vector Machine Classifier and a 10-fold internal cross-validation approach, which demonstrated a predictive accuracy of 89% with an AUC of 0.89 for each IDH mutant and IDH wild subgroup.ConclusionA machine learning classifier of radiomic features extracted from multiparametric MRI images (T1C and T2w) provides important diagnostic information for the non-invasive prediction of the IDH mutant or wild-type phenotype of high-grade gliomas and may have potential use in either escalating or de-escalating adjuvant therapy for gliomas or for using targeted agents in the future.

GLCM and CNN Deep Learning Model for Improved MRI Breast Tumors Detection

Breast cancer is one of the most common types of cancer among Iraqi women. MRI has been used in the detection of breast tumors for its efficient performance in the diagnosis process providing high accuracy. In this paper, breast MRI image data from 89 patients were classified using GLCM and CNN feature extraction methods. Four models were evaluated consisting of GLCM, CNN, combined GLCM and CNN features based models. The statistical ANOVA feature selection method was used to reduce the redundant features. The reduced feature subset was fed to CNN classifier for obtaining either normal or abnormal breast images. The proposed method was assessed in terms of accuracy, precision, recall and F1-score. The model provided 100% classification accuracy.

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.

GLCM Based Locally Feature Extraction On Natural Image

GLCM is a feature extraction method that uses statistical analysis using a gray scale. Contrast, correlation, energy and entropy are feature features whose value will be sought as the basis for finding the threshold which can then be used to find the threshold value in image segmentation. In this study, a local-based GLCM method is used where the image that has been made into grayscale will be divided into 16 parts of the same size. Each section will look for the value of its GLCM features, namely Contrast, correlation, energy and entropy. The calculation of these four features will be applied to 16 parts of the grayscale image, which can then be used to find the threshold value. The results of the four features in the calculation with an angle of 0o are the contrast value = 0.0080, correlation = 0.619, energy : 0.00160 and entropy : 0.05591.

High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms

We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test (p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805–0.887) and 0.807 (0.752–0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.

Analysis of Skin disease techniques using Smart Phone and Digital Camera Identification of Skin Disease

Skin diseases are a serious health issue that affects a large number of individuals. In recent years, with the fast advancement of technology and the use of various data mining approaches, dermatological predictive classification has become increasingly predictive and accurate. It is more help to dermatologist to identify the disease, As a result, the development of machine learning approaches capable of efficiently. The purpose of this study is making an application of identification skin disease images by using the machines learning method, Support Vector Machine (SVM), and KNN techniques. The image processes and machine learning is performed early detection of skin diseases. The aim of this study is determined the classification of skin diseases in humans. Each skin disease has symptoms. It has five skin diseases such as Acne, Psoriasis, Wrath, Psoriasis, and Ulcer. We have collected 314 skin disease images from the government of hospital, Aurangabad with the help of mobile camera and Sony HD camera. Gaussian Filter is used for image pre-processing. The segmentation method is used for K-Means Clustering and the feature extraction method are used for feature extraction. We have used Haar feature, color feature, FCM, OS-FCM, GLCM and LBF features for classifications. Based on the result, the SVM is given 92% accuracy for haar feature, FCM and OS-FCM. and KNN classifier, K-Means are given 89% and 89% accuracy using mobile phone camera dataset. The SVM, KNN and K-Means are given 91%, 87% and 89% accuracy respectively using Sony HD camera dataset. SVM is given good result in both dataset.

Improving Path Loss Prediction Using Environmental Feature Extraction from Satellite Images: Hand-Crafted vs. Convolutional Neural Network

There is an increased exploration of the potential of wireless communication networks in the automation of daily human tasks via the Internet of Things. Such implementations are only possible with the proper design of networks. Path loss prediction is a key factor in the design of networks with parameters such as cell radius, antenna heights, and the number of cell sites that can be set. As path loss is affected by the environment, satellite images of network locations are used in developing path loss prediction models such that environmental effects are captured. We developed a path loss model based on the Extreme Gradient Boosting (XGBoost) algorithm, whose inputs are numeric (non-image) features that influence path loss and features extracted from images composed of four tiled satellite images of points along the transmitter to receiver path. The model can predict path loss for multiple frequencies, antenna heights, and environments such that it can be incorporated into Radio Planning Tools. Various feature extraction methods that included CNN and hand-crafted and their combinations were applied to the images in order to determine the best input features, which, when combined with non-image features, will result in the best XGBoost model. Although hand-crafted features have the advantage of not requiring a large volume of data as no training is involved in them, they failed in this application as their use led to a reduction in accuracy. However, the best model was obtained when image features extracted using CNN and GLCM were combined with the non-image features, resulting in an RMSE improvement of 9.4272% against a model with non-image features only without satellite images. The XGBoost model performed better than Random Forest (RF), Extreme Learning Trees (ET), Gradient Boosting, and K Nearest Neighbor (KNN) based on the combination of CNN, GLCM, and non-image features. Further analysis using the Shapley Additive Explanations (SHAP) revealed that features extracted from the satellite images using CNN had the highest contribution toward the XGBoost model’s output. The variation in values of features with output path loss values was presented using SHAP summary plots. Interactions were also observed between some features based on their dependence plots from the computed SHAP values. This information, when further explored, could serve as the basis for the development of an explainable/glass box path loss model.

A Machine learning Classification approach for detection of Covid 19 using CT images

Coronavirus disease 2019 popularly known as COVID 19 was first found in Wuhan, China in December 2019. World Health Organization declared Covid 19 as a transmission disease. The symptoms were cough, loss of taste, fever, tiredness, respiratory problem. These symptoms were likely to show within 11 –14 days. The RT-PCR and rapid antigen biochemical tests were done for the detection of COVID 19. In addition to biochemical tests, X-Ray and Computed Tomography (CT) images are used for the minute details of the severity of the disease. To enhance efficiency and accuracy of analysis/detection of COVID images and to reduce of doctors' time for analysis could be addressed through Artificial Intelligence. The dataset from Kaggle was utilized to analyze. The statistical and GLCM features were extracted from CT images for the classification of COVID and NON-COVID instances in this study. CT images were used to extract statistical and GLCM features for categorization. In the proposed/prototype model, we achieved the classification accuracy of 91%, and 94.5% using SVM and Random Forest respectively.

A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure

The adoptability of the heart to external and internal stimuli is reflected by heart rate variability (HRV). Reduced HRV can be a predictor of post-infarction mortality. In this study, we propose an automated system to predict and diagnose congestive heart failure using short-term heart rate variability analysis. Based on the nonlinear, nonstationary, and highly complex dynamics of congestive heart failure, we extracted multimodal features to capture the temporal, spectral, and complex dynamics. Recently, the Bayesian inference approach has been recognized as an attractive option for the deeper analysis of static features, in order to perform a comprehensive analysis of extracted nodes (features). We computed the gray level co-occurrence (GLCM) features from congestive heart failure signals and then ranked them based on ROC methods. This study focused on utilizing the dissimilarity feature, which is ranked as highly important, as a target node for the empirical analysis of dynamic profiling and optimization, in order to explain the nonlinear dynamics of GLCM features extracted from heart failure signals, and distinguishing CHF from NSR. We applied Bayesian inference and Pearson’s correlation (PC). The association, in terms of node force and mapping, was computed. The higher-ranking target node was used to compute the posterior probability, total effect, arc contribution, network profile, and compression. The highest value of ROC was obtained for dissimilarity, at 0.3589. Based on the information-gain algorithm, the highest strength of the relationship was obtained between nodes “dissimilarity” and “cluster performance” (1.0146), relative to mutual information (81.33%). Moreover, the highest relative binary significance was yielded for dissimilarity for 1/3rd (80.19%), 2/3rd (74.95%) and 3/3rd (100%). The results revealed that the proposed methodology can provide further in-depth insights for the early diagnosis and prognosis of congestive heart failure.

Optimal knee osteoarthritis diagnosis using hybrid deep belief network based on Salp swarm optimization method

AbstractOsteoarthritis (OA) damages the articular cartilage of the knee and is a severe degenerative joint condition. Currently, OA diagnosis is carried out by symptom analysis and progressive evaluation of the radiographs, although the method is subjective. This work presents a novel computer aided diagnostic system to diagnose the severity of the disease in its early stages. The proposed method comprises of stages that include image pre‐processing, extraction of features based on discrete wavelet decomposition, histogram, GLCM and texture features, and classification by stacked up‐RBM Deep Belief Networks (Hybrid DBN) finally. The performance of HDBN is improved by optimizing the hyper parameters with the Salp Swarm Optimization Algorithm (SSA). Experiments conducted on the Kaggle database consisting of 7503 images demonstrated an overall accuracy of 99.45% with 100% sensitivity and specificity. The impact on isolated and combined feature contributions is also analyzed using 10‐fold cross validation (CV). The robustness of the algorithm is tested on degraded images of salt‐pepper, Gaussian, and Poisson noise, which proved the effectiveness of the method. Comparison of the method proposed with similar techniques is done by employing different optimization algorithms and classifiers.

Classification of Covid-19 Based on a Combination of GLCM and Deep Features by Using X-Ray Images

Koronavirüs salgınının (Covid-19) tüm dünyayı etkisi altına alması ile Covid-19 gibi viral hastalıklar için acil ancak doğru ve hızlı teşhis yöntemlerine ihtiyaç duyulmuştur. Covid-19’un ortaya çıkması ile birlikte Covid-19’un tespit edilmesi için tıp doktorları tarafından akciğer tomografi ve X-Ray görüntüleri kullanılmaya başlanmıştır. Geleneksel ve modern makine öğrenimi yaklaşımlarının X-Ray ve tomografi görüntüleri kullanılarak hastalık teşhisi için kullanıldığı bilinmektedir. Bu yönü ile yapay zekaya dayalı uygulamalar alan uzmanlarına benzer ve hatta neredeyse daha iyi performanslar ortaya koyarak sektöre katkı sağlamaktadır. Bu çalışmada X-Ray akciğer görüntüleri kullanılarak hastalık teşhisi için derin ve geleneksel doku analizi özniteliklerinin kombinasyonuna dayalı hibrit bir destek vektör makineleri (SVM) sınıflandırma modeli önerilmektedir. Çalışmada kullanılan veri seti, sağlıklı, Covid-19, viral pnömoni ve akciğer opasitesi hastalarının X-Ray akciğer görüntülerinden oluşmaktadır. X-Ray görüntülerinden elde edilen hibrit öznitelikler Gri Seviye Eş-Oluşum Matrisi (GLCM) ve DenseNet-201 derin sinir ağı kullanılarak elde edilmiştir. Hibrit özniteliklerin performansı, geleneksel bir yaklaşım olarak GLCM öznitelikleri ile karşılaştırılmıştır. Her iki öznitelik SVM ile eğitilmiştir. Sınıflandırma başarısında ortalama %99.2 doğruluk değerine ulaşılmıştır. Elde edilen diğer performans ölçütleri de hibrit özniteliklerin geleneksel yönteme göre daha başarılı olduğunu göstermektedir. Covid-19 teşhisi için önerilen yapay zekâ tabanlı yöntemin umut verici olduğu görülmüştür.

Analysis of Vascular Architecture and Parenchymal Damage Generated by Reduced Blood Perfusion in Decellularized Porcine Kidneys Using a Gray Level Co-occurrence Matrix.

There is no cure for kidney failure, but a bioartificial kidney may help address this global problem. Decellularization provides a promising platform to generate transplantable organs. However, maintaining a viable vasculature is a significant challenge to this technology. Even though angiography offers a valuable way to assess scaffold structure/function, subtle changes are overlooked by specialists. In recent years, various image analysis methods in radiology have been suggested to detect and identify subtle changes in tissue architecture. The aim of our research was to apply one of these methods based on a gray level co-occurrence matrix (Topalovic et al.) computational algorithm in the analysis of vascular architecture and parenchymal damage generated by hypoperfusion in decellularized porcine. Perfusion decellularization of the whole porcine kidneys was performed using previously established protocols. We analyzed and compared angiograms of kidneys subjected to pathophysiological arterial perfusion of whole blood. For regions of interest Santos et al. covering kidney medulla and the main elements of the vascular network, five major GLCM features were calculated: angular second moment as an indicator of textural uniformity, inverse difference moment as an indicator of textural homogeneity, GLCM contrast, GLCM correlation, and sum variance of the co-occurrence matrix. In addition to GLCM, we also performed discrete wavelet transform analysis of angiogram ROIs by calculating the respective wavelet coefficient energies using high and low-pass filtering. We report statistically significant changes in GLCM and wavelet features, including the reduction of the angular second moment and inverse difference moment, indicating a substantial rise in angiogram textural heterogeneity. Our findings suggest that the GLCM method can be successfully used as an addition to conventional fluoroscopic angiography analyses of micro/macrovascular integrity following in vitro blood perfusion to investigate scaffold integrity. This approach is the first step toward developing an automated network that can detect changes in the decellularized vasculature.

AFD filter and E2N2 classifier for improving visualization of crop image and crop classification in remote sensing image

ABSTRACT Due to increasing global population, the need of focusing on land cover and land uses by human beings is felt. Nowadays, most of the researchers and scientists are found showing much attention in agricultural landscapes because the effectual land-cover (LC) mapping provides the correct information regarding the land uses and land cover. Crop classification is one of the essential issues in the agriculture field because it affects the government and private agencies economically. The number of methodologies exist to classify the crop images from the remotely sensed image, but the traditional methodologies do not perform well due to some factors, like blurring effect, noise, etc. We have proposed an Adaptive Fuzzy-Centred Denoising Filter (AFDF) and Extended Elman Neural Network (E2N2) to enhance visualization of crop image to crop classification in order to overcome traditional techniques RSI. The remote-sensing crop images have been considered in the proposed work, and the AFDF preprocesses the image to eradicate the noise. After that, the Jaccard Coefficient-based Shared Nearest Neighbour (JCSNN) segments the object as preprocessed images. The LTrP, DWT, GLCM, and shape features are extracted as the optimal features chosen by Enhanced Elephant Herding Optimization (E2HO). Lastly, the crop is classified using the E2N2 classifier. The high-resolution images were utilized for the experiments to validate the proposed work. At last, various techniques have been compared against the proposed system, and outcomes have shown significant enhancement in accuracy and reliability. The proposed system obtains 88.01% F-measure, 95.2% accuracy with lesser FPR values that are greater when analogized to all prevalent methods. The advantage of using proposed effective protocols, like Adaptive Fuzzy-centred Denoising Filter and also E2N2 classifier, boost-up the crop images’ visualization and also crop categorization in remote-sensing images.

Spatial-Temporal-Circulated GLCM and Physiological Features for In-Vehicle People Sensing Based on IR-UWB Radar

In-vehicle people sensing has received considerable attention for preventing overloading and forgotten children. Impulse radio ultrawideband (IR-UWB) radar is widely applied in noncontact sensing applications. However, metal-rich environment and narrow space with multiple objects bring dense multipaths in received radar signals, posing a great challenge to in-vehicle sensing, especially people counting and motion recognition. This article proposes novel spatial-temporal-circulated gray level co-occurrence matrix (STC-GLCM) and physiological features for people sensing, to detect the presence, number, and motions of people in the vehicle. Considering temporal consistency and spatial continuity of highly associated in-vehicle radar signals, STC-GLCM feature is designed to describe the 2-D circular spatial-temporal distribution, and extracted at individual, associated, and global scales. In addition, the singular value ratio (SVR) is introduced to select the physiological feature at each individual scale, representing the possibility and intensity of cardiopulmonary activity. Three different classifiers are applied on these combined features for classification, and the stochastic gradient descent (SGD) is adopted for performance analysis. A radar signal dataset is constructed for various in-vehicle scenarios, including different number of people in a stationary and moving vehicle with several motions. The accuracies reach 97.5% and 97.0% in counting the number of people in the stationary and moving vehicle, and 99.0% in recognizing the motions, demonstrating the effectiveness and robustness of these proposed features. The proposed in-vehicle radar signal dataset is available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/yangxiuzhu777/In-Vehicle-Radar-Data</uri></i> .

A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

Skin diseases effectively influence all parts of life. Early and accurate detection of skin cancer is necessary to avoid significant loss. The manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color levels. Therefore, an automated system to identify these skin diseases is required. Few studies on skin disease classification using different techniques have been found. However, previous techniques failed to identify multi-class skin disease images due to their similar appearance. In the proposed study, a computer-aided framework for automatic skin disease detection is presented. In the proposed research, we collected and normalized the datasets from two databases (ISIC archive, Mendeley) based on six Basal Cell Carcinoma (BCC), Actinic Keratosis (AK), Seborrheic Keratosis (SK), Nevus (N), Squamous Cell Carcinoma (SCC), and Melanoma (M) common skin diseases. Besides, segmentation is performed using deep Convolutional Neural Networks (CNN). Furthermore, three types of features are extracted from segmented skin lesions: ABCD rule, GLCM, and in-depth features. AlexNet transfer learning is used for deep feature extraction, while a support vector machine (SVM) is used for classification. Experimental results show that SVM outperformed other studies in terms of accuracy, as AK disease achieved 100% accuracy, BCC 92.7%, M 95.1%, N 97.8%, SK 93.1%, SCC 91.4% with a global accuracy of 95.4%.

Execution Analysis of Clarity Locale Segmentation for Condition Recognition Utilizing Genetic Algorithm Method

The collection of fluid at the back of the fetal neck, known as nuchal translucency (NT), is linked to chromosomal abnormalities and early heart failure in the first trimester of pregnancy. Using the Co-Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification algorithm, this research presents an effective way for recognising and localising the NT region in fetus images in which noise removed. Then, pattern features are extracted Initially, the noises in fetus images are detected and eliminated using directional filtering technique and then Gabor transform from the magnitude of Gabor transformed fetus image and then they are optimized using Genetic Algorithm (GA) approach. The extracted GLCM, ELBP and LTP features are integrated into feature vector for further classifications. The size of constructed feature vector is high and leads to high computation time for the classification process. These optimized feature set is classified using CANFIS. Finally, the graph cut segmentation method is used for segmenting the NT region. This proposed method is practically used in many health care centers in rural areas.