Gray Level Co-occurrence Matrix Research Articles

Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability.

To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast magnetic resonance imaging (MRI) to improve upon conventional risk assessment models in determining culprit lesions. Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). They underwent MRI within 2 weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features. Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone [area under curve (AUC), 0.819 ± 0.002 vs. 0.689 ± 0.019 respectively, p = 0.014]. The radiomic model alone also provided value over the conventional model [AUC, 0.805 ± 0.003 vs. 0.689 ± 0.019 respectively, p = 0.031]. T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix (GLCM), Grey Level Dependence Matrix (GLDM) and Grey Level Size Zone Matrix (GLSZM) sub-types were particularly useful in identifying textures which could detect vulnerable lesions. The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischemic cerebrovascular events.

Ultrasound Super Resolution Imaging for Accurate Uterus Tumor Detection and Malignancy Prediction

The term ‘tumor’ describes an atypical development of cells that forms a mass inside an organ; depending on the organ's invasive nature and propensity for metastasis, the growth may be benign or malignant. Improving patient outcomes requires the early detection of malignant tumors. Despite its lower resolution and noise problems, ultrasound, which is frequently used to diagnose uterine tumors, is safer and more economical than magnetic resonance imaging (MRI) scans and biopsies. Ultrasound pictures can be processed using methods including denoising, enhancement, segmentation, and feature extraction to get around these restrictions and boost their quality. Enhanced ultrasound images can reach even higher accuracy, cementing them as plausible alternatives to MRI.A comparative analysis of copious relevant image de-speckling, image enhancement, segmentation, and feature extraction methods are carried out. Higher resolution and superior quality, strong segmented real-time ultrasound uterus tumour images are produced by using diffusion-based hybrid filters, Super Resolution Convolutional Neural Networks (SRCNN), and U-net segmentation technique. The Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used to extract textural features. With the help of several machine learning approaches, such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Random Forest classifiers (RFC), the extracted characteristics are immediately sent to classifiers to classify uterus tumours from ultrasound images between benign and malignant. For the categorization of uterine tumors, the RFC classifier outperformed the other classifiers. The viability of cancer detection using ultrasound pictures is significantly strengthened by the suggested machine learning methodology. Multiple hospitals provided data on ultrasound pictures of uterine tumors, which were used to develop the model and obtain the prediction findings. A radiologist with 17 years of expertise in diagnostic radiology further assessed this dataset. We could produce high-quality ultrasonic real-time images of uterine tumor datasets with the suggested machine learning model at a 97.8% accuracy rate utilizing RFC.

Textural indices of aerial images reveal urban landscapes temporal dynamics and their effects on plants and birds

Landscape temporal dynamics are a crucial concern in ecology to understand current biodiversity. But historical environmental data are often not easily available. This study assesses for the first time the potential of historical orthophotographs to describe past landscape conditions and determine the temporal lag between landscape changes and biotic communities responses.This method utilizes Gray level Co-occurrence Matrix (GLCM) texture indices computed from black and white orthophotographs to construct continuous metrics of landscape composition. Metrics were first developed using present-day data, i.e. 2015, and calibrated with categorical land cover maps of the Strasbourg Eurometropolis, France. Subsequently, these metrics were applied to historical orthophotographs from 1966, 1976, 1986, and 2000. Plant and bird data from research and citizen science programs were used to estimate the time delay in which these communities respond to evolutions in built areas and high vegetation.Obtained texture-based models reveal that built areas exhibit high contrast and homogeneity, depicted through a linear relation, and that high vegetation display low pixel brightness and high brightness diversity, better described via a nonlinear model. We successfully applied those findings to historical orthophotographs, and revealed dependencies on landscape composition up to 50 years ago for plants and up to 30 years ago for birds, with the time lag and the influence of built and high vegetation areas depending on the selected biodiversity indices.These results demonstrate the utility of archive black and white orthophotographs' texture indices for describing urban landscapes over the past five decades, making them valuable tools for ecological research. These indices are more accessible than categorical data like land cover maps for past years. They have the potential to greatly benefit future studies investigating time lags in landscape ecology, simplifying access to historical landscape features and contributing to sustainable urban planning efforts.

Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix

Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix

Digital Image Object Detection with GLCM Multi-Degrees and Ensemble Learning

Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours (K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting objects when the object experiences rotation which results in significant visualization variations. In this research, the GLCM method is used to produce features that are stable against rotation. Furthermore, classification methods such as K-Nearest Neighbours (KNN), Random Forest (RF), and KNN-RF fusion using the Voting ensemble method are evaluated to improve detection accuracy. The experimental results show that the use of multi-degrees and the use of ensemble voting at all degrees can increase the accuracy value, and the highest accuracy for extraction using multi-degrees is 95.95%. Based on test results which show that the use of features of various degrees and the ensemble voting method can increase accuracy for detecting objects experiencing rotation  

Image splicing detection using low-dimensional feature vector of texture features and Haralick features based on Gray Level Co-occurrence Matrix

Digital image forgery has become hugely widespread, as numerous easy-to-use, low-cost image manipulation tools have become widely available to the common masses. Such forged images can be used with various malicious intentions, such as to harm the social reputation of renowned personalities, to perform identity fraud resulting in financial disasters, and many more illegitimate activities. Image splicing is a form of image forgery where an adversary intelligently combines portions from multiple source images to generate a natural-looking artificial image. Detection of image splicing attacks poses an open challenge in the forensic domain, and in recent literature, several significant research findings on image splicing detection have been described. However, the number of features documented in such works is significantly huge. Our aim in this work is to address the issue of feature set optimization while modeling image splicing detection as a classification problem and preserving the forgery detection efficiency reported in the state-of-the-art. This paper proposes an image-splicing detection scheme based on textural features and Haralick features computed from the input image’s Gray Level Co-occurrence Matrix (GLCM) and also localizes the spliced regions in a detected spliced image. We have explored the well-known Columbia Image Splicing Detection Evaluation Dataset and the DSO-1 dataset, which is more challenging because of its constituent post-processed color images. Experimental results prove that our proposed model obtains 95% accuracy in image splicing detection with an AUC score of 0.99, with an optimized feature set of dimensionality of 15 only.

Fuzzy Deep Learning Recurrent Neural Network Algorithm to Detect Corn Leaf Disease

Corn is a major commodity after rice in supporting food self-sufficiency in Indonesia. However, due to leaf disease, the quality and quantity of corn plants are greatly reduced. The problem with detecting corn leaf diseases is that the detection method is still manual, making it inefficient and ineffective. Therefore, in this study, disease detection on corn leaves was performed using the Fuzzy C-Means (FCM) and Long Short-Term Memory (LSTM) methods. First, oversampling was carried out to ensure an equal amount of data in all classes, then the corn leaf images were pre-processed before being input into the LSTM algorithm. After completing clustering process in the [Formula: see text] algorithm, the next step involved extracting texture features using the Gray Level Co-occurrence Matrix (GLCM) technique, followed by classification using LSTM. To assess their performance, both algorithms underwent evaluation using the k-fold cross-validation method, and their accuracy and speed were compared. The results of the k-fold cross-validation demonstrated that the [Formula: see text] algorithm achieved an accuracy of 63.53%, whereas the LSTM algorithm achieved an accuracy of 80.24%. In terms of the time required for training and prediction, the LSTM algorithm took 13[Formula: see text]min and 18[Formula: see text]s for training on corn leaf disease images, while the prediction process only took 1.59[Formula: see text]s. The training and prediction time required for the [Formula: see text] algorithm were 65[Formula: see text]min and 24[Formula: see text]s and 5[Formula: see text]min and 44[Formula: see text]s, respectively. The conclusion of this study is that the LSTM algorithm has better accuracy and time compared to [Formula: see text] on the dataset used in this study in terms of corn leaf disease detection.

Crop classification in Google Earth Engine: leveraging Sentinel-1, Sentinel-2, European CAP data, and object-based machine-learning approaches

ABSTRACT In contemporary agriculture and environmental management, the need for precise and accurate crop maps has never been more vital. Although object-based (OB) methods within Google Earth Engine (GEE) improve accuracy and output quality in contrast to pixel-based approaches, their application to crop classification remains relatively rare. Therefore, this study aimed to develop an OB classification methodology for crops located in central Italy’s Lake Trasimeno area. This methodology employed spectral bands, spectral indices (Normalized Difference Vegetation Index and Modified Radar Vegetation Index), and textural information (Gray-Level Co-occurrence Matrix) derived from Sentinel-2 L2A (S2) and Sentinel-1 GRD (S1) data within the GEE platform. Moreover, European Common Agricultural Policy (CAP) data associated with cadastral parcels were employed and served as ground information during the training and validation stages. The CAP crop classes were aggregated into three levels (Level 1–3 crop types, Level 2–5 crop types, and Level 3–7 crop types). Subsequently, optimized Random Forest (RF) classifiers were applied to map crops effectively. Feature selection analysis highlighted the importance of certain textural features. Additionally, findings demonstrated high overall accuracy results (89% for Level 1, 86% for Level 2, and 82% for Level 3). It was found that winter crops achieved the highest F-score at Level 1, while specific subclasses, such as winter cereals and warm-season cereals, excelled at Level 2. Overall, this study provides a promising approach for improved crop mapping and precision agriculture in the GEE environment.

Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture

Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.

The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture

The availability of high-resolution satellite imagery has boosted the modelling of tropical forest attributes based on texture metrics derived from grey-level co-occurrence matrices (GLCMs). This procedure has shown that GLCM metrics are good predictors of vegetation attributes. Nonetheless, the procedure is also sensitive to the scale of analysis (image resolution and plot size). This study aimed to analyse the effect of spatial scale on the modelling of forest attributes, and to provide some ecological insight into such effect. Nineteen 32 × 32 m sampling plots were used to quantify forest structure (basal area: BA; mean height: H; standard deviation of height, HSD; density, D; and aboveground biomass, AGB). The 19 plots were subdivided into four 16 × 16 m, one of which was subdivided into four 8 × 8 m plots. To match this design, 12 GLCM metrics were calculated from a GeoEye-1 image (pixel size ≤ 2 m) using a 5-, 9-, and 21-pixel window from the R, NIR, NDVI, and EVI bands. For each of the windows, we modelled the five structural variables as linear combinations of the 12 metrics through linear models. The modelling potential ranged from high (R2 = 0.70) to low (0.11). H was the best-predicted attribute; this occurred at the smallest scale, with increasing scales producing lower R2 values. The second best-predicted attribute was HSD, which peaked at the intermediate scale. D and AGB displayed a similar pattern. BA was the only attribute best predicted at the largest scale. Thus, in predicting tropical forest attributes from GLCM-derived texture metrics, the spatial scale to be used should reflect the spatial scale at which ecological processes occur. Therefore, understanding how ecological processes express themselves in a remotely sensed image becomes a critical task.

Non-invasive coronary artery disease identification through the iris and bio-demographic health profile features using stacking learning

This study proposes a non-invasive method for predicting Coronary Artery Disease (CAD) using iris analysis, patient data, and Machine Learning (ML), primarily with iris images. It involved 281 participants, comprising 155 CAD patients and 126 non-patient controls, with eye images and biodemographic data collected at a Cardiology outpatient clinic. The study explored three scenarios: Scenario-I focused on biodemographic data, Scenario-II on iris features, and Scenario-III combined iris images and data. Iris processing included location determination, normalization, and heart region selection, with image enhancement via adaptive histogram equalization. Feature extraction through a 2-level wavelet transform generated 272 attributes, including statistical, Gray Level Co-occurrence Matrix, and Gray Level Run Length Matrix features for eight subcomponents. Correlation-based selection identified the best features, and classification employed ML techniques and incorporated stacking learning to enhance the results. Scenario-I achieved the highest accuracy at 83.57% among all evaluated algorithms. In Scenario-II, the proposed algorithm consistently outperformed others, achieving 94.88% accuracy and strong performance in other metrics, highlighting its effectiveness. In Scenario-III, the algorithm maintained superiority with 96.07% accuracy, specificity, recall, and area under the curve values. The proposed algorithm consistently outperforms other methods across scenarios, indicating its potential for CAD diagnosis, making it a promising choice for future CAD systems. The proposed algorithm presents a novel approach to the preliminary diagnosis of CAD, eliminating the necessity for electrocardiography, echocardiography, or effort tests. It also enables seamless integration into telemedicine systems, allowing for tele-diagnosis to conduct preliminary assessments before routine clinical practice.

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound

Objective. Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line. Approach. The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images. Main results. We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P < 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios. Significance. The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

An Intelligent Computer Aided Diagnosis System for Classification of Ovarian Masses using Machine Learning Approach

Ovarian cancer, a difficult and often asymptomatic malignancy, remains a substantial global health concern in women. An ovary is a female reproductive organ, which lies on each side of the uterus and used to store eggs. Computer-aided diagnosis (CAD) is an approach that involves using computer algorithms and machine learning techniques to assist medical professionals in diagnosing ovarian malignancies, benign tumors or Poly-cystic ovaries (PCOS). The need for models that can effectively predict benign ovarian tumors and ovarian cancer has led to the use of machine learning techniques. Our research objective is to propose a machine learning-based system for accurate and early ovarian mass detection utilizing novel annotated ovarian masses. We have used an actual patient database whose input features were extracted from 187 transvaginal ultrasound images from database. The input image is preprocessed using the Block Matching 3D filter. The process involves employing binary and watershed segmentation techniques, followed by the integration of Gabor, Gray-Level Co-Occurrence Matrix (GLCM), Tamura, and edge feature extraction methods. K-Nearest Neighbors (KNN) and Random Forest (RF) are two classifiers used for classification. Based on our results, we are able to demonstrate that binary segmentation with RF classifiers is more accurate (above 86%) than KNN classifiers (under 84%).

Grading the Dominant Pathological Indices in Liver Diseases from Pathological Images Using Radiomics Methods

Purpose: This study aims to diagnose the severity of important pathological indices, i.e., fibrosis, steatosis, lobular inflammation, and ballooning from the pathological images of the liver tissue based on extracted features by radiomics methods.&#x0D; Materials and Methods: This research uses the pathological images obtained from liver tissue samples for 258 laboratory mice. After preprocessing the images and data augmentation, a collection of texture feature sets extracted by gray-level-based algorithms, including Global, Gray-level Co-Occurrence Matrix (GLCM), Gray-level Run length Matrix (GLRLM), Gray-level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM) algorithms. Then, advanced methods of classification, namely Support Vector Machine (SVM), Random Forest (RF), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Multi-layer Perceptrons (MLP) are employed. This procedure is provided separately for each of the four indices of fibrosis level in 6 grading classes, steatosis in 5 grading classes, inflammation in 4 grading classes, and ballooning in 3 grading classes. For a comparison of the output of these algorithms, the accuracy value obtained from the evaluation data is presented for the performance of different methods.&#x0D; Results: The results showed that, compared to other methods, the Gaussian SVM algorithm provides a better response to the classification of the grading of liver disease among all the indices from the pathological images due to its structural features. This value of accuracy was calculated at 84.30% for fibrosis, 90.55% for steatosis, 81.11% for inflammation, and 95.98% for ballooning.&#x0D; Conclusion: This fully automatic framework based on advanced radiomics algorithms and machine learning from pathological images can be very useful in clinical procedures and be considered as an assistant or a substitute for pathologists’ diagnoses.

Visual coating inspection framework via self-labeling and multi-stage deep learning strategies

AbstractAn instantaneous and precise coating inspection method is imperative to mitigate the risk of flaws, defects, and discrepancies on coated surfaces. While many studies have demonstrated the effectiveness of automated visual inspection (AVI) approaches enhanced by computer vision and deep learning, critical challenges exist for practical applications in the manufacturing domain. Computer vision has proven to be inflexible, demanding sophisticated algorithms for diverse feature extraction. In deep learning, supervised approaches are constrained by the need for annotated datasets, whereas unsupervised methods often result in lower performance. Addressing these challenges, this paper proposes a novel deep learning-based automated visual inspection (AVI) framework designed to minimize the necessity for extensive feature engineering, programming, and manual data annotation in classifying fuel injection nozzles and discerning their coating interfaces from scratch. This proposed framework comprises six integral components: It begins by distinguishing between coated and uncoated nozzles through gray level co-occurrence matrix (GLCM)-based texture analysis and autoencoder (AE)-based classification. This is followed by cropping surface images from uncoated nozzles, and then building an AE model to estimate the coating interface locations on coated nozzles. The next step involves generating autonomously annotated datasets derived from these estimated coating interface locations. Subsequently, a convolutional neural network (CNN)-based detection model is trained to accurately localize the coating interface locations. The final component focuses on enhancing model performance and trustworthiness. This framework demonstrated over 95% accuracy in pinpointing the coating interfaces within the error range of ± 6 pixels and processed at a rate of 7.18 images per second. Additionally, explainable artificial intelligence (XAI) techniques such as t-distributed stochastic neighbor embedding (t-SNE) and the integrated gradient substantiated the reliability of the models.

SCDC-Net: Detection and Classification of Skin Cancer using Deep q Neural Network with Hybrid Features

The increment in radiation in atmosphere causing wide spread of skin cancer disease around the globe. Cancer of the skin is one of the most lethal forms of the illness, and it is responsible for millions of deaths annually all over the world. Because of this, the early identification of skin cancer using computer-aided diagnostic (CAD) technology might potentially save a person's life.  However, skin cancer classification of performance is affected by hair artifacts and improper segmentation of lesion. The conventional methods were failed to achieve higher performance due to improper feature analysis. Therefore, to achieve the robust performance, this work was developed the transfer learning-based skin cancer detection and classification (SCDC-Net). Initially, UG-Net model was introduced to eliminate hair from skin lesions by enhancing the region of interest, which is developed by combining U-shaped deep learning network (U-Net) with generative adversarial networks (GAN). In addition, Hybrid U-Net (HU-Net) perform the segmentation operation for identifying the disease effected region of interest. Further, the features are extracted using gray level cooccurrence matrix (GLCM) matrix and discrete wavelet transform (DWT), which extracts the color, texture, statistical features. Finally, these combined characteristics are used to train a deep q neural network (DQNN) model, which then classifies a number of different skin cancer illness categories. The simulations conducted on real-time ISIC-2019 dataset proved that proposed SCDC-Net outperformed in all performance measures as compared to existing methods.

A machine vision approach for detecting changes in drill core textures using optical images

Drill core images offer valuable insights into the texture, structure and mineralogy of ores and their host rocks, which can be used to optimise downstream processes in the mining industry. The impact on downstream processes from particles of similar composition and mineralogy but different textures has been examined by several previous researchers through the application of supervised machine-learning techniques. This study proposes a novel approach for detecting changes in drill core textures through the analysis of optical images. This approach compares three widely used image feature extraction techniques (local binary patterns, grey-level co-occurrence matrix and convolutional neural network), followed by calculation of a uniqueness measure, based on the Hotelling statistic, designed to identify anomalous segments of core. The effectiveness of the uniqueness measure is validated on a test core comprising six sections with different textures. Two drill cores, from the Brukunga test site in South Australia, were selected as case studies. Of the three feature extraction methods, local binary patterns were found to give the strongest signals of change. There exist two main regimes that separate halfway along both drill cores, indicating a change in lithology or the presence of mineralisation.

Differential diagnosis of cemento-osseous dysplasia and periapical cyst using texture analysis of CBCT

BackgroundRadiolucencies found at the root apex in patients with cemento-osseous dysplasia (COD) may be mistaken for periapical cysts (PC) of endodontic origin. The purpose of this study was to examine the utility of quantitative texture analysis using cone-beam computed tomography (CBCT) to differentiate between COD and PC.MethodsPatients who underwent CBCT at Wonkwang University Daejeon Dental Hospital between January 2019 and December 2022 and were diagnosed with COD and PC by clinical, radiologic, and, if necessary, histopathologic examination were included. Twenty-five patients each were retrospectively enrolled in the COD and PC group. All lesions observed on axial CBCT images were manually segmented using the open-access software MaZda version 4.6 to establish the regions of interest, which were then subjected to texture analysis. Among the 279 texture features obtained, 10 texture features with the highest Fisher coefficients were selected. Statistical analysis was performed using the Mann-Whitney U-test, Welch’s t-test, or Student’s t-test. Texture features that showed significant differences were subjected to receiver operating characteristics (ROC) curve analysis to evaluate the differential diagnostic ability of COD and PC.ResultsThe COD group consisted of 22 men and 3 women, while the PC group consisted of 14 men and 11 women, showing a significant difference between the two groups in terms of sex (p=0.003). The 10 selected texture features belonged to the gray level co-occurrence matrix and included the sum of average, sum of entropy, entropy, and difference of entropy. All 10 selected texture features showed statistically significant differences (p<0.05) when comparing patients with COD (n=25) versus those with PC (n=25), osteolytic-stage COD (n=11) versus PC (n=25), and osteolytic-stage COD (n=11) versus cementoblastic-stage COD (n=14). ROC curve analysis to determine the ability to differentiate between COD and PC showed a high area under the curve ranging from 0.96 to 0.98.ConclusionTexture analysis of CBCT images has shown good diagnostic value in the differential diagnosis of COD and PC, which can help prevent unnecessary endodontic treatment, invasive biopsy, or surgical intervention associated with increased risk of infection.

Cytoarchitectonic Analysis and 3D Maps of the Mesial Piriform Region in the Human Brain

The mesial piriform region plays a central role in olfaction. Its small size and complex geometry, however, make it a difficult target in functional neuroimaging studies, while histological maps often represent schematic drawings, which are not compatible with requirements for modern imaging. To bridge this gap, cytoarchitectonic analysis and mapping of the region was performed in serial histological sections over their full extent in 10 postmortem brains. The temporobasal areas PirTBd and PirTBv and temporal areas PirTu and PirTit were identified and analyzed. Probabilistic cytoarchitectonic maps of the piriform areas in MNI reference space and high-resolution maps of the amygdala-piriform region on the BigBrain model were calculated as part of the Julich-Brain. Differences in the cytoarchitectonic “texture” of the region were quantified based on the Gray Level Co-Occurrence Matrix. Results showed that allocortical areas were not consistently associated with the rostral Limen insulae, although it was often suggested as a landmark in neuroimaging protocols. PirTu was associated with the uncal tip. PirTit was the largest area, reaching to the temporal pole, with a “temporal” (caudal) and a “temporopolar” (rostral) part having complex neighborhood relationships. The probabilistic maps reflect interindividual variability; they are openly available via the digital EBRAINS platform to serve as an anatomical reference for studies related to olfaction.

Hybrid Semiconductor Wafer Inspection Framework via Autonomous Data Annotation

Abstract Semiconductors play an indispensable role in data collection, processing, and analysis, ultimately enabling more agile and productive operations. Given the importance of wafers in semiconductor fabrication, the purity of a wafer is essential to maintain the integrity of the overall manufacturing process. To tackle this issue, this study proposes a novel Automated Visual Inspection (AVI) framework for scrutinizing semiconductor wafers from scratch, capable of both identifying defective wafers and pinpointing the location of defects through autonomous data annotation. Initially, this proposed methodology leveraged a texture analysis method known as Gray Level Co-occurrence Matrix (GLCM) that categorized wafer images—captured via a stroboscopic imaging system—into distinct scenarios for clear and noisy wafer inspection. GLCM approaches further allowed for a complete separation of noisy wafers into defective and normal wafers as well as the extraction of defect images from noisy defective wafers, which were then used for training a Convolutional Neural Network (CNN) model. Consequently, the CNN model excelled in localizing defects on noisy defective wafers, achieving an F1 score exceeding 0.901. In clear wafers, a background subtraction technique represented defects as clusters of white points. The quantity of these white points not only determined the defectiveness of clear wafers but also pinpointed locations of defects on clear wafers. Lastly, the application of a CNN further enhanced performance, robustness, and consistency irrespective of variations in the ratio of white point clusters. This technique demonstrated accuracy in localizing defects on clear wafers, yielding an F1 score greater than 0.993.