Image Texture Features Research Articles

A hyperdimensional framework: Unveiling the interplay of RBP and GSN within CNNs for ultra-precise brain tumor classification

This study presents the RBP-CNN model, a convolutional neural network specifically designed for the precise classification of brain tumors in medical imaging. Conventional methods often encounter difficulties in extracting image noise and texture features, which has led to the incorporation of regional binary patterns (RBP) and Gray Standard Normalization (GSN) preprocessing techniques in CNN. The research addresses fundamental inquiries regarding the impact of the model on accuracy, false classifications, and efficiency. The novelty of RBP-CNN lies in its distinctive approach to extracting texture features, which involves optimizing pixel values through GSN preprocessing and generating regional binary patterns based on integral images. The objective of this research is to bridge a critical gap by providing a more accurate and efficient model for classifying brain tumors. The key findings reveal the exceptional performance of RBP-CNN, achieving a classification accuracy of 96% with a reduced false classification ratio of 7% across a dataset of 3000 samples. Comparative analyses position RBP-CNN as superior to alternative models in terms of accuracy, false classification rates, and efficiency. The structural insights and hyperparameter values of the model, as well as its application to the FigShare dataset, demonstrate its robustness and scalability. RBP-CNN emerges as an innovative and effective solution, advancing the field of medical image categorization. The findings of this study contribute a novel methodology, paving the way for future exploration in hyperspectral image applications and positioning RBP-CNN as a potential state-of-the-art tool for medical image analysis.

Exploring the Differences in Tree Species Classification between Typical Forest Regions in Northern and Southern China

Focusing on the trend of continuously seeking high-precision tree species classification results in small areas from the perspectives of sensors and classification algorithms. This study aimed to explore the effects of data sources, classifiers, and seasons on classification accuracy in regions with significant environmental variation, examining patterns of tree species classification to enhance the transferability of classification. Considering two typical forest distribution regions in the north and south of China, this study utilized the revisitation cycle and open-source advantages of Sentinel-2 and Landsat-8. Leveraging the Google Earth Engine (GEE) platform, this study captured spectral features, vegetation indices, and texture features for single seasonal and seasonal combination images. With the assistance of Sentinel-1A and SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), backscattering coefficient features and topographical features were extracted and input with features captured from Sentinel-2 and Landsat-8 into three types of classifiers: random forest (RF), support vector machine (SVM), and gradient tree boosting (GTB) for major tree species classification. In this research, we discovered that the best classification for single season in the northern study area was spring, whereas, for the southern study area, it was winter. Seasonal combination images effectively improved the classification accuracy of single seasonal images, with Sentinel-2 imagery displaying better classification performance compared to Landsat-8, and the optimal classifier differing between the north and the south. The inclusion of topographical or backscattering coefficient features in the four-season combination imagery contributed to improvements in classification accuracy, with topographical features significantly enhancing the classification performance in the topographically varied southern study area. The evaluation of feature importance indicated that elevation was the most critical feature for classification, while spectral features and vegetation indices were also significant. In the southern study area with large topographical discrepancies, subdividing into different terrain units led to improved tree species classification accuracy in medium-altitude, gentle slope areas. These findings provide insights into the regularity of enhancing tree species classification accuracy in environmentally diverse areas through the use of multi-source remote sensing data and multi-seasonal imagery. Consequently, the results offer a reference for the identification of tree species across large areas and the creation of spatial distribution maps.

Detection of jelly orange granulation disease using a dual-input Resnet-Transformer model (DresT) based on acoustic vibration images and a novel acoustic vibration device

Granulation is a common internal disease in citrus fruits, and it is difficult to distinguish fruits with granulation disease from their appearance. In this study, a novel acoustic vibration device based on a micro-LDV, a microphone and a resonance speaker was employed to collect acoustic vibration response signals of "Aiyuan 38" jelly orange. The one-dimensional acoustic vibration response signal was converted into acoustic vibration images, and a double-input Resnet-Transformer network (DresT) was constructed for extracting deep features in acoustic vibration images for identifying jelly-orange granulation disease. Firstly, train Drest and Resnet50 models using acoustic vibration images and compare the performance of Drest with that of Resnet50 (based on CNN). Then PLS-DA and SVM models are trained using acoustic vibration image texture features or acoustic vibration spectral features, and the performance is compared with the DresT model. The results showed that the DresT model trained using acoustic vibration images can accurately identify jelly orange granulation disease with a detection accuracy of 99.31 %. The F1 of the model is 99.5 %, the accuracy is 99.01 %, and the recall is 100 %.

TD Swin-UNet: Texture-Driven Swin-UNet with Enhanced Boundary-Wise Perception for Retinal Vessel Segmentation.

Retinal vessel segmentation plays a crucial role in medical image analysis, aiding ophthalmologists in disease diagnosis, monitoring, and treatment guidance. However, due to the complex boundary structure and rich texture features in retinal blood vessel images, existing methods have challenges in the accurate segmentation of blood vessel boundaries. In this study, we propose the texture-driven Swin-UNet with enhanced boundary-wise perception. Firstly, we designed a Cross-level Texture Complementary Module (CTCM) to fuse feature maps at different scales during the encoding stage, thereby recovering detailed features lost in the downsampling process. Additionally, we introduced a Pixel-wise Texture Swin Block (PT Swin Block) to improve the model's ability to localize vessel boundary and contour information. Finally, we introduced an improved Hausdorff distance loss function to further enhance the accuracy of vessel boundary segmentation. The proposed method was evaluated on the DRIVE and CHASEDB1 datasets, and the experimental results demonstrate that our model obtained superior performance in terms of Accuracy (ACC), Sensitivity (SE), Specificity (SP), and F1 score (F1), and the accuracy of vessel boundary segmentation was significantly improved.

RMSRGAN: A Real Multispectral Imagery Super-Resolution Reconstruction for Enhancing Ginkgo Biloba Yield Prediction

Multispectral remote sensing data with abundant spectral information can be used to compute vegetation indices to improve the accuracy of Ginkgo biloba yield prediction. The limited spatial resolution of multispectral cameras restricts the detail capture over wide farmland, but super-resolution (SR) reconstruction methods can enhance image quality. However, most existing SR models have been trained on images processed from downsampled high-resolution (HR) images, making them less effective in reconstructing real low-resolution (LR) images. This study proposes a GAN-based super-resolution reconstruction method (RMSRGAN) for multispectral remote sensing images of Ginkgo biloba trees in real scenes. A U-Net-based network is employed instead of the traditional discriminator. Convolutional block attention modules (CBAMs) are incorporated into the Residual-in-Residual Dense Blocks (RRDBs) of the generator and the U-Net of the discriminator to preserve image details and texture features. An unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to capture field multispectral remote sensing images of Ginkgo biloba trees at different spatial resolutions. Four matching HR and LR datasets were created from these images to train RMSRGAN. The proposed model outperforms the traditional models by achieving superior results in both quantitative evaluation metrics (peak signal-to-noise ratio (PSNR) is 32.490, 31.085, 27.084, 26.819, and structural similarity index (SSIM) is 0.894, 0.881, 0.832, 0.818, respectively) and qualitative evaluation visualization. Furthermore, the efficiency of our proposed method was tested by generating individual vegetation indices (VIs) from images taken before and after reconstruction to predict the yield of Ginkgo biloba. The results show that the SR images exhibit better R2 and RMSE values than LR images. These findings show that RMSRGAN can improve the spatial resolution of real multispectral images, increasing the accuracy of Ginkgo biloba yield prediction and providing more effective and accurate data support for crop management.

Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics

Arctic sea ice leads are linear fractures in pack ice, which provide narrow windows for the enhanced exchange of mass, energy, and momentum between the atmosphere and ocean. However, the parameterisations of the sub-grid (< 1 km) lead distribution and its associated dynamic processes are still challenging for numerical sea ice modelling. This study explores the dynamics governing lead formation in the Arctic Ocean using multiple data sources including Sentinel-1 synthetic aperture radar (SAR) images, buoy array, and atmospheric reanalysis. Random forest (RF) models trained based on the polarisation and texture features of SAR images were compared, and an optimal RF model sensitive to narrow leads was selected with implementation of screening based on lead geometry. As a case study, the lead distribution along the trajectory of buoy array in the central Arctic Ocean during the freezing period of 2018–2019 was obtained. An enhanced lead opening was observed as the sea ice motion swerved from clockwise circulation following the Beaufort Gyre (BG) to meridional advection when assembled into the Transpolar Drift (TPD). Synoptically, active lead-opening events associated with ice divergence were driven by episodic cyclones. We noticed a dramatic change in the backscatter signal over the leads caused by the growth of thin ice and formation of frost flowers. The identification and the evolution of narrow leads during the freezing season given in this study are conducive to increasing our understanding of the response mechanism of sea ice to atmospheric dynamic processes and supporting the numerical simulation of leads.

Quantitative analysis of lung lesions using unenhanced chest computed tomography images.

Chest radiograph and computed tomography (CT) scans can accidentally reveal pulmonary nodules. Malignant and benign pulmonary nodules can be difficult to distinguish without specific imaging features, such as calcification, necrosis, and contrast enhancement. However, these lesions may exhibit different image texture characteristics which cannot be assessed visually. Thus, a computer-assisted quantitative method like histogram analysis (HA) of Hounsfield unit (HU) values can improve diagnostic accuracy, reducing the need for invasive biopsy. In this exploratory control study, nonenhanced chest CT images of 20 patients with benign (10) and cancerous (10) lesion were selected retrospectively. The appearances of benign and malignant lesions were very similar in chest CT images, and only pathology report was used to discriminate them. Free hand region of interest (ROI) was inserted inside the lesion for all slices of each lesion. Mean, minimum, maximum, and standard deviations of HU values were recorded and used to make HA. HA showed that the most malignant lesions have a mean HU value between 30 and 50, a maximum HU less than 150, and a minimum HU between -30 and 20. Lesions outside these ranges were mostly benign. Quantitative CT analysis may differentiate malignant from benign lesions without specific malignancy patterns on unenhanced chest CT image.

Prediction and modeling of tie-dye polyester fabric's texture and color feature in supercritical carbon dioxide

To mitigate the water-intensive challenges of tie-dye clothing production and address the absence of objective evaluation for finished products, we have explored the correlation between tie-dye techniques in supercritical carbon dioxide (SC-CO2) and image patterns. Employing digital image processing, we extracted the average HSV three-component value of the valid tie-dye area, the proportion of incompletely dyed area in the HSV color space, and the initial three texture features of the digital tie-dye image using the Tamura method. We scrutinized five factors in the supercritical CO2 dyeing process: rotational speed, dyeing temperature, dyeing pressure, dye concentration, and dyeing time. The single-factor climbing test revealed that temperature and pressure are the predominant factors influencing tie-dyeing performance. We propose a correlation between the texture parameters of tie-dyeing and the swelling effect of polyester fabric in a supercritical CO2 environment. Subsequently, we conducted two-factor tests on temperature and pressure to investigate their specific effects on the parameters of supercritical polyester tie-dyeing. The results indicated significant changes in regularity, saturation, and the proportion of undyed area with variations in dyeing temperature and pressure. Building upon these findings, we developed an artificial neural network for predicting tie-dye dyeing temperature and pressure in supercritical CO2, utilizing features regularity, saturation, and proportion of undyed area as inputs. In the 105st round, experimental results demonstrated an accuracy of 0.99445, substantiating the efficacy of tie-dye pattern features in supercritical CO2 for technique forecasting.

Research on the Vanishing Point Detection Method Based on an Improved Lightweight AlexNet Network for Narrow Waterway Scenarios

When an unmanned surface vehicle (USV) navigates in narrow waterway scenarios, its ability to detect vanishing points accurately and quickly is highly important for safeguarding its navigation safety and realizing automated navigation. We propose a novel approach for detecting vanishing points based on an improved lightweight AlexNet. First, a similarity evaluation calculation method based on image texture features is proposed, by which some scenarios are selected from the filtered Google Street Road Dataset (GSRD). These filtered scenarios, together with the USV Inland Dataset (USVID), compose the training dataset, which is manually labeled according to a non-uniformly distributed grid level. Next, the classical AlexNet was adjusted and optimized by constructing sequential connections of four convolutional layers and four pooling layers and incorporating the Inception A and Inception C structures in the first two convolutional layers. During model training, we formulate vanishing point detection as a classification problem using an output layer with 225 discrete possible vanishing point locations. Finally, we compare and analyze the labeled vanishing point with the detected vanishing point. The experimental results show that the accuracy of our method and the state-of-the-art algorithmic vanishing point detector improves, indicating that our improved lightweight AlexNet can be applied in narrow waterway navigation scenarios and can provide a technical reference for autonomous navigation of USVs.

A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices

The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.

Semantic-aware normalizing flow with feature fusion for image anomaly detection

In the field of computer vision, anomaly detection is a binary classification task used to identify exceptional instances within image datasets. Typically, it can be divided into two aspects: texture defect detection and semantic anomaly detection. Existing methods often use pre-trained feature extractors to singly capture semantic or spatial features of images, and then employ different classifiers to handle these two types of anomaly detection tasks. However, these methods fail to fully utilize the synergistic relationship between these two types of features, resulting in algorithms that excel in one type of anomaly detection task but perform poorly in the other type. Therefore, we propose a novel approach that successfully combines these two types of features into a normalizing flow learning module to address both types of anomaly detection tasks. Specifically, we first adopt a pre-trained Vision Transformer (ViT) model to capture both texture and semantic features of input images. Subsequently, using the semantic features as input, we design a novel normalizing flow model to fit the semantic distribution of normal data. In addition, we introduce a feature fusion module based on attention mechanisms to integrate the most relevant texture and semantic information between these two types of features, significantly enhancing the model’s ability to simultaneously represent the spatial texture and semantic features of the input image. Finally, We conduct comprehensive experiments on well-known semantic and texture anomaly detection datasets, namely Cifar10 and MVTec, to evaluate the performance of our proposed method. The results demonstrate that our model achieves outstanding performance in both semantic and texture anomaly detection tasks, particularly achieving state-of-the-art results in semantic anomaly detection.

New Radiological Corticalization Index as an Indicator of Implant Success Rate Depending on Prosthetic Restoration-5 Years of Follow-Up.

The new Radiological Corticalization Index (CI) is an indicator that describes bone remodeling near the dental implant's neck at the pixel level and is not visible to the naked eye. The aim of this research was to evaluate the correlation between the CI and bone remodeling using only radiographic (RTG) images. RTG samples were divided into groups depending on prosthetic restoration; the implant neck area around dental implants was examined, and texture features of the RTG images were analyzed. The study also investigated the type of prosthetic restoration and its influence as a factor on bone structure. The statistical analysis included evaluating feature distribution, comparing means (t-test) or medians (W-test), and performing a regression analysis and one-way analysis of variance or the Kruskal-Wallis test, as no normal distribution or between-group variance was indicated for the significant differences in the investigated groups. Differences or relationships were considered statistically significant at p < 0.05. The research revealed correlations between single crowns, overdenture restoration, bridge restoration, platform switching, prosthetic fracture, CI, and also marginal bone loss where p was lower than 0.05. However, the corticalization phenomenon itself has not yet been fully explored. The findings suggest that, depending on the type of prosthetic restoration, the corticalization index may correlate with marginal bone loss or not. Further research is necessary, as the index is suspected to not be homogeneous.

Failure precursors recognition method for loading coal and rock using the fracture texture features of infrared thermal images

Early warning of the catastrophic failure of rocks is a challenging rock mechanics problem. This article proposed a failure precursor recognition method based on infrared thermal image texture features for coal and rock. Based on spatiotemporal background noise correction for infrared thermal images, a new thermal image parameter of loading coal and rock, Contrast Texture Feature Values (CTFV), is proposed to extract subtle changes in the thermal image caused by crack evolution based on the Gray Level Co-occurrence Matrix (GLCM). The cumulative Crack Texture Thermal Image (CTTI) is reconstructed using CTFV, which can accurately reflect the spatial evolution process of loading coal and rock cracks. The CTFV remains at 0 in the early stage of loading and gradually increases with stress increase at the unstable crack propagation stage, which can serve as a reference for precursor warning of coal and rock failure. For shale, sandstone, and limestone, the precursor of CTFV at grayscale levels of 7, 8, and 9, can be classified into high failure risk, medium failure risk, and low failure risk, respectively. For coal samples, the CTFV is only applicable for the critical warning of high failure risk when the grayscale level is 7. Then, an adaptive identification method for failure precursors based on the sliding window probability density estimation method is proposed. The research results can enhance the reliability of IR monitoring technology for rock failure and instability early warning and can provide support for the prevention and control of rock engineering and geological disasters.

Multiclassification of Hepatic Cystic Echinococcosis by Using Multiple Kernel Learning Framework and Ultrasound Images

To properly treat and care for hepatic cystic echinococcosis (HCE), it is essential to make an accurate diagnosis before treatment. ObjectiveThe objective of this study was to assess the diagnostic accuracy of computer-aided diagnosis techniques in classifying HCE ultrasound images into five subtypes. MethodsA total of 1820 HCE ultrasound images collected from 967 patients were included in the study. A multi-kernel learning method was developed to learn the texture and depth features of the ultrasound images. Combined kernel functions were built-in Support Vector Machine (MK-SVM) for the classification work. The experimental results were evaluated using five-fold cross-validation. Finally, our approach was compared with three other machine learning algorithms: the decision tree classifier, random forest, and gradient boosting decision tree. ResultsAmong all the methods used in the study, the MK-SVM achieved the highest accuracy of 96.6% on the fused feature set. ConclusionThe multi-kernel learning method effectively learns different image features from ultrasound images by utilizing various kernels. The MK-SVM method, which combines the learning of texture features and depth features separately, has significant application value in HCE classification tasks.

An Image Retrieval Method for Lunar Complex Craters Integrating Visual and Depth Features

In the geological research of the Moon and other celestial bodies, the identification and analysis of impact craters are crucial for understanding the geological history of these bodies. With the rapid increase in the volume of high-resolution imagery data returned from exploration missions, traditional image retrieval methods face dual challenges of efficiency and accuracy when processing lunar complex crater image data. Deep learning techniques offer a potential solution. This paper proposes an image retrieval model for lunar complex craters that integrates visual and depth features (LC2R-Net) to overcome these difficulties. For depth feature extraction, we employ the Swin Transformer as the core architecture for feature extraction and enhance the recognition capability for key crater features by integrating the Convolutional Block Attention Module with Effective Channel Attention (CBAMwithECA). Furthermore, a triplet loss function is introduced to generate highly discriminative image embeddings, further optimizing the embedding space for similarity retrieval. In terms of visual feature extraction, we utilize Local Binary Patterns (LBP) and Hu moments to extract the texture and shape features of crater images. By performing a weighted fusion of these features and utilizing Principal Component Analysis (PCA) for dimensionality reduction, we effectively combine visual and depth features and optimize retrieval efficiency. Finally, cosine similarity is used to calculate the similarity between query images and images in the database, returning the most similar images as retrieval results. Validation experiments conducted on the lunar complex impact crater dataset constructed in this article demonstrate that LC2R-Net achieves a retrieval precision of 83.75%, showcasing superior efficiency. These experimental results confirm the advantages of LC2R-Net in handling the task of lunar complex impact crater image retrieval.

Exploring the diagnostic value of ultrasound radiomics for neonatal respiratory distress syndrome

BackgroundNeonatal respiratory distress syndrome (NRDS) is a prevalent cause of respiratory failure and death among newborns, and prompt diagnosis is imperative. Historically, diagnosis of NRDS relied mostly on typical clinical manifestations, chest X-rays, and CT scans. However, recently, ultrasound has emerged as a valuable and preferred tool for aiding NRDS diagnosis. Nevertheless, evaluating lung ultrasound imagery necessitates rigorous training and may be subject to operator-dependent bias, limiting its widespread use. As a result, it is essential to investigate a new, reliable, and operator-independent diagnostic approach that does not require subjective factors or operator expertise. This article aims to explore the diagnostic potential of ultrasound-based radiomics in differentiating NRDS from other non-NRDS lung disease.MethodsA total of 150 neonatal lung disease cases were consecutively collected from the department of neonatal intensive care unit of the Quanzhou Maternity and Children’s Hospital, Fujian Province, from September 2021 to October 2022. Of these patients, 60 were diagnosed with NRDS, whereas 30 were diagnosed with neonatal pneumonia, meconium aspiration syndrome (MAS), and transient tachypnea (TTN). Two ultrasound images with characteristic manifestations of each lung disease were acquired and divided into training (n = 120) and validation cohorts (n = 30) based on the examination date using an 8:2 ratio. The imaging texture features were extracted using PyRadiomics and, after the screening, machine learning models such as random forest (RF), logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and multilayer perceptron (MLP) were developed to construct an imaging-based diagnostic model. The diagnostic efficacy of each model was analyzed. Lastly, we randomly selected 282 lung ultrasound images and evaluated the diagnostic efficacy disparities between the optimal model and doctors across differing levels of expertise.ResultsTwenty-two imaging-based features with the highest weights were selected to construct a predictive model for neonatal respiratory distress syndrome. All models exhibited favorable diagnostic performances. Analysis of the Youden index demonstrated that the RF model had the highest score in both the training (0.99) and validation (0.90) cohorts. Additionally, the calibration curve indicated that the RF model had the best calibration (P = 0.98). When compared to the diagnostic performance of experienced and junior physicians, the RF model had an area under the curve (AUC) of 0.99; however, the values for experienced and junior physicians were 0.98 and 0.85, respectively. The difference in diagnostic efficacy between the RF model and experienced physicians was not statistically significant (P = 0.24), whereas that between the RF model and junior physicians was statistically significant (P < 0.0001).ConclusionThe RF model exhibited excellent diagnostic performance in the analysis of texture features based on ultrasound radiomics for diagnosing NRDS.

Global–local feature learning for fine-grained food classification based on Swin Transformer

Separable object parts, such as the head and tail in a bird, are vital for fine-grained visual classifications. For those objects without separable parts, the classification task relies only on local and global textural image features. Although the Swin Transformer architecture was proposed to efficiently capture both local and global visual features, it still exhibits a bias towards global features. Therefore, our goal is to enhance the local feature learning capability of the Swin Transformer by adding four new modules of the Local Feature Extraction Network (L-FEN), Convolution Patch-Merging (CP), Multi-Path (MP), and Multi-View (MV). The L-FEN enhances the Swin transformer with the improved local feature capture. The CP is a localized and hierarchical adaptation of the Swin’s Patch Merging technique. The MP method integrates features across various Swin stages to accentuate local details. Meanwhile, the MV Swin transformer block supersedes traditional Swin blocks with those incorporating varied receptive fields, ensuring a broader scope of local feature capture. Our enhanced architecture, named Global–Local Swin Transformer (GL-Swin), is applied to solve a fine-grained food classification task. On three major food datasets: ISIA Food-500 UEC Food-256, and Food-101, our GL-Swin achieved accuracies of 66.75%, 85.78%, and 92.93% respectively, consistently outperforming other leading methods.

Evaluation of Uterine Carcinosarcoma and Uterine Endometrial Carcinoma Using Magnetic Resonance Imaging Findings and Texture Features.

Aim This study aimed to evaluate the diagnostic feasibility of magnetic resonance imaging (MRI) findings and texture features (TFs) for differentiating uterine endometrial carcinoma from uterine carcinosarcoma. Methods This retrospective study included 102 patients who were histopathologically diagnosed after surgery with uterine endometrial carcinoma (n=68) or uterine carcinosarcoma (n=34) between January 2008 and December 2021. We assessed conventional MRI findings and measurements (cMRFMs) and TFs on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) map, as well as their combinations, in differentiating between uterine endometrial carcinoma and uterine carcinosarcoma. The least absolute shrinkage and selection operator (LASSO) was used to select three features with the highest absolute value of the LASSO regression coefficient for each model and construct a discriminative model. Binary logistic regression analysis was used to analyze the disease models and conduct receiver operating characteristic analyses on the cMRFMs, T2WI-TFs, ADC-TFs, and their combined model to compare the two diseases. Results A total of four models were constructed from each of the three selected features. The area under the curve (AUC) of the discriminative model using these features was 0.772, 0.878, 0.748, and 0.915 for the cMRFMs, T2WI-TFs, ADC-TFs, and a combined model of cMRFMs and TFs, respectively. The combined model showed a higher AUC than the other models, with a high diagnostic performance (AUC=0.915). Conclusion A combined model using cMRFMs and TFs might be helpful for the differential diagnosis of uterine endometrial carcinoma and uterine carcinosarcoma.

A high accuracy method for the sintering condition recognition of rotary kiln

According to the different material sintering conditions, the sintering conditions of alumina rotary kiln can be divided into: super-heated, super-chilled, and normal. In this paper, based on Local Binary Pattern (LBP) and the primary-color method, a novel feature extraction method is proposed to obtain information about temperature in flame images without calibrating camera parameters. Through the analysis of the problem as well as the experimental phenomena, a new classification procedure is devised: in the first step, the super-chilled condition is first separated, in the second step, the normal and super-heat condition are classified. Different feature extraction methods are used in the two steps mentioned above. One is to extract the texture features of pseudo temperature images, and short-time energy is used to describe the dynamic features. The other is to extract the texture features of grey-images by our improved LBPP,Rriu2, and characterize the dynamics with sample entropy and variance. Finally, experimental results show that the our feature extraction method can effectively reduce intra-class variation, increase inter-class variation and receive a high classification accuracy.

Research on surface roughness detection and prediction of ti-6Al-4v titanium alloy based on multi-feature fusion

Considering that titanium alloy is a critical aviation resource, ensuring its machining quality is of significant importance. Surface roughness remains a key parameter for surface quality inspection. This article introduces a multi-sensor titanium alloy milling monitoring system aimed at accurately monitoring surface quality during titanium alloy processing. Principal component analysis is conducted on three-dimensional milling force and vibration. We propose a multi-objective cutting parameter optimization procedure to simultaneously optimize multiple cutting parameters to improve surface roughness and tool life. Considering the high dependence of visual measurement on the light source, we strive to mitigate this limitation and improve prediction accuracy by considering one-dimensional and two-dimensional feature values. We establish a multidimensional signal feature surface roughness prediction system based on milling parameters, milling vibration, milling force, and texture image features. Using particle swarm algorithm and machine learning models, the undetermined parameters in the prediction system are obtained. The results show that the prediction accuracy of the multi-signal feature fusion surface roughness prediction system is 99.12%, with a mean square error of less than 0.01. The research can provide some theoretical guidance for the accurate monitoring of the surface quality of titanium alloy processing.