2D image-based electrical contact surface degradation and its health assessment

One of the most common causes of death worldwide is skin cancer and its incidence is increasing. To achieve optimal treatment and improve clinical outcomes for patients, precision skin cancer detection and classification approaches are required, which can be achieved through the application of feature extraction and machine learning algorithms. The development of such algorithms to identify important patterns from skin cancer image datasets enables early detection and more accurate classification and more effective treatment. Although previous studies have tried to detect skin cancer using feature extraction techniques such as HFF, HOG, and GLCM, some weaknesses still need to be improved. This research aims to combine various feature extraction methods such as Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns and machine learning algorithms such as Support Vector Machine, Random Forest, and Gaussian Naïve Bayes in the classification process between Melanoma and Nevus skin cancers. In this research, the number of datasets used is 17,397 derived from the ISIC Dataset. The results showed that the Histogram Oriented Gradients method with Support Vector Machine algorithm achieved the highest accuracy of 92%. The combination of Gray Level Co-occurrence Matrix and Local Binary Patterns with Random Forest algorithm also achieved an accuracy of 92%, the combination of Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns with Random Forest algorithm also resulted in an accuracy of 92%. These findings suggest that the combination of various feature extraction methods and machine learning algorithms can improve accuracy in skin cancer classification, which in turn can contribute to early detection and more effective treatment.

Diabetic Retinopathy (DR) is a microangiopathic complication of diabetes mellitus that can cause visual impairment to permanent blindness. Early detection of DR is essential to prevent disease progression, but conventional methods require time, cost, and expertise that are not always available. This study aims to compare the performance of the Random Forest (RF) and Support Vector Machine (SVM) algorithms in DR classification based on texture features extracted from retinal fundus images. The dataset used consists of 3,000 retinal fundus images obtained from the Kaggle platform, divided into 2,400 training data and 600 test data. Image preprocessing includes conversion to grayscale, resizing to a resolution of 128×128 pixels, and normalization. Feature extraction is performed using a combination of Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) to produce a 14-dimensional feature vector. Performance evaluation uses accuracy, precision, recall, F1-score, ROC curve, and 5-fold cross-validation metrics. The results showed that Random Forest significantly outperformed SVM with an accuracy of 96% compared to 64%, an AUC value of 0.99 compared to 0.72, and an average cross-validation accuracy of 94.5% compared to 63.42%. Random Forest also showed balanced performance in both classes with precision, recall, and F1-score of 0.96, while SVM experienced classification imbalance especially in the disease class. This study proves that Random Forest is a more optimal algorithm for an automatic DR detection system based on fundus image texture features and can support increasing the accessibility of DR screening in areas with limited specialist medical personnel.

Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

Category: Midfoot/Forefoot Introduction/Purpose: Surgical planning based on angular measurements obtained on conventional radiographs is challenging due to perspective distortion and operator bias. Novel weightbearing CT (WBCT) three-dimensional (3D) measurements using coordinate systems may represent a more reliable and accurate evaluation of this 3D deformity. The objective of this study was to compare the M1-M2 intermetatarsal angle (IMA) obtained manually on WBCT digitally reconstructed 2D radiographs versus a set of coordinates from the full 3D dataset, in patients with hallux valgus (HV) deformity and in healthy controls. We hypothesised that the 3D measurements would be more reliably obtained, demonstrating different values when compared to 2D radiographic measurements. Methods: In this multicenter retrospective comparative study, 83 feet that underwent WBCT of the foot were included (41 HV: mean age 59, 81% female, 42 controls: mean age 52, 80% female). Datasets were analysed by three independent trained foot and ankle surgeons using the same protocol. Coordinates in three planes (x, y, z) of four different landmark points were harvested: center of the heads and midpoint of the proximal metaphysis of the 1st and 2nd metatarsal. The IMA measurements were then performed in reconstructed radiographic images (DRR-IMA). The data collected was then analyzed by a single 4th independent and blinded investigator who calculated the 3D angle (3D-IMA) and its projection on the weightbearing plane (2D-IMA). Intra-observer realiability was assessed by Pearson/Spearman’s correlation. Intermethod correlation was evaluated by intraclass correlation coefficient (ICC). Mean values for measures were comparared by One-way ANOVA. P-values of less than 0.05 were considered significant. Results: Intraobserver reliability was excellent for radiographic DRR-IMA (0.95) and 3D coordinates assessment (0.99). Intermethod correlation between the three different imaging modalities (DDR, 2D and 3D), considering bias and interactions, were respectively 0.71 and 0.51 in control and HV patients. IMA measurements were found to be similar when measured in DRR, 2D and 3D WBCT images, for both controls and HV patients. Mean values and confidence intervals (CI) for controls were 8.8 degrees (CI, 7.9-9.7) in DDR images, 9.8 degrees (CI, 8.7-10.9) in 2D images and 10.6 degrees (CI, 9.5-11.8) in 3D images. When compared to controls, HV patients demonstraded significantly increased IMA (p<0.05): 13.06 degrees (CI, 11.8-14.3) in DDR images, 12.1 degrees (CI, 10.8-13.3) in 2D images and 13.3 degrees (CI, 12.3-14.3) in 3D images. Conclusion: We found that similar values for IMA were measured in 2D reconstructed radiographs, WBCT 3D and 2D projected images. When compared to controls, HV patients were found to have increased IMA in all three different imaging types used (DDR, 2D and 3D). Intermethod correlation was higher for IMA performed in controls. Intraobserver reliability was excellent for both radiographic IMA measurements and WBCT 3D coordinates. Our study is the first study to evaluate measurements of the 3D-IMA in HV and control patients. Further investigations are required before guidelines for its clinical use can be formulated.

Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of $$88.5\%$$ , HOG with Fine-KNN achieves $$86.5\%$$ accuracy and LBP with LDA achieves the accuracy of $$97.9\%$$ .

The purpose of our study was to estimate the ability of structural MRI-based texture analysis (TA) to quantify the textural changes in corpus callosum (CC) caused by Progressive Supranuclear Palsy (PSP). Three distinct TA approaches: gray level co-occurrence matrix (GLCM), local binary pattern (LBP), oriented Gaussian derivative filter-bank (O-GDFB) were exploited and compared to present a novel study that can classify PSP ( n=16) by quantifying the textural changes in CC versus that in healthy controls (HC, n=16 using T1-W MRI. Different statistical texture features were extracted using GLCM and LBP, from the five distinct CC sub-regions: CC1 (genu), CC2 (anterior-mid), CC3 (mid-body), CC4 (posterior-mid) and CC5 (splenium). Unlike statistical based TA, a modified GDFB based TA was also performed to explore the differences in filter responses in the same CC sub-regions for different orientations and scales. Finally, texture features that showed statistically highest discrimibility were input to the SVM classifier in classifying PSP and HC. With leave-one-out cross validation, SVM classification results maximum accuracy of 88%, 94% and 84% at CC3 using GLCM, LBP and O-GDFB based TA respectively. The diagnostic potential of CC TA using structural MRI can be applied on routine radiology scan without needing any additional acquisition for quantitative analysis. Significant texture alteration found at CC3 may be of great importance in the diagnosis and understanding of this pathology.

Mapping of native plant species and noxious weeds is critical to monitor grassland degradation in the Three-River Headwaters Region. The grass species in the study area were divided into native plant species and noxious weeds based on the applicability of grazing. Field data of the two kinds of grass species were collected and divided into ten coverage grades with an interval of 10%. The eight original bands were used to derive 37 features by Random Forest (RF) algorithm, including first derivative (FD), vegetation indexes (VI), biochemical indexes (BI), hat transform (KT) and gray level co-occurrence matrix (GLCM). The importance of each feature was calculated and 17 of them were selected by RF, reflecting their superiority in identifying native plant species and noxious weeds. The random forest algorithm was also used in classification of the native plant species with an overall accuracy (OA) of 43.2% (8 bands), 45.9% (37 features) and 51.3% (17 selected features) and an 10% grade expansion accuracy (GEA) of 59.4%, 64.8% and 70.2%, respectively. The noxious weeds with a higher overall accuracy (OA) of 62.1% (8 bands), 64.8% (37 features) and 67.5% (17 selected features) and an 10% grade expansion accuracy (GEA) of 86.4%, 83.7% and 89.1%, respectively. Therefore, the classification of native plant species and noxious weeds coverage grades with the interval of 10% demonstrated the potential of the WorldView-2 data for mapping native plant species and noxious weeds in the typical area of Three-River Headwaters Region.

Accurate evaluation of femoral offset is difficult with conventional anteroposterior (AP) X-rays. The EOS imaging system is a system that makes the acquisition of simultaneous and orthogonal AP and lateral images of the patient in the standing position possible. These two-dimensional (2D) images are equivalent to standard plane X-rays. Three-dimensional (3D) reconstructions are obtained from these paired images according to a validated protocol. This prospective study explores the value of the EOS imaging system for comparing measurements of femoral offset from these 2D images and the 3D reconstructions. We included 110 patients with unilateral total hip arthroplasty (THA). The 2D offset was measured on the AP view with the same protocol as for standard X-rays. The 3D offset was calculated from the reconstructions based on the orthogonal AP and lateral views. Reproducibility and repeatability studies were conducted for each measurement. We compared the 2D and 3D offset for both hips (with and without THA). For the global series (110 hips with and 110 without THA), 2D offset was 40 mm (SD 7.3; 7-57 mm). The standard deviation was 6.5 mm for repeatability and 7.5 mm for reproducibility. Three-dimensional offset was 43 mm (SD 6.6; 22-62 mm), with a standard deviation of 4.6 for repeatability and 5.5 for reproducibility. Two-dimensional offset for the hips without THA was 40 mm (SD 7.0; 26-56 mm), and 3D offset 43 mm (SD 6.6; 28-62 mm). For THA side, 2D offset was 41 mm (SD 8.2; 7-57 mm) and 3D offset 45 mm (SD 4.8; 22-61 mm). Comparison of the two protocols shows a significant difference between the 2D and 3D measurements, with the 3D offset having higher values. Comparison of the side with and without surgery for each case showed a 5-mm deficit for the offset in 35 % of the patients according to the 2D measurement but in only 26 % according to the 3D calculation. This study points out the limitations of 2D measurements of femoral offset on standard plane X-rays. The reliability of the EOS 3D models has been previously demonstrated with CT scan reconstructions as a reference. The EOS imaging system could be an option for obtaining accurate and reliable offset measurements while significantly limiting the patient's exposure to radiation.

To distinguish malignant pulmonary nodules from benign ones is of much importance in computer-aided diagnosis of lung diseases. Compared to many previous methods which are based on shape or growth assessing of nodules, this proposed three-dimensional (3D) texture feature based approach extracted fifty kinds of 3D textural features from gray level, gradient and curvature co-occurrence matrix, and more derivatives of the volume data of the nodules. To evaluate the presented approach, the Lung Image Database Consortium public database was downloaded. Each case of the database contains an annotation file, which indicates the diagnosis results from up to four radiologists. In order to relieve partial-volume effect, interpolation process was carried out to those volume data with image slice thickness more than 1mm, and thus we had categorized the downloaded datasets to five groups to validate the proposed approach, one group of thickness less than 1mm, two types of thickness range from 1mm to 1.25mm and greater than 1.25mm (each type contains two groups, one with interpolation and the other without). Since support vector machine is based on statistical learning theory and aims to learn for predicting future data, so it was chosen as the classifier to perform the differentiation task. The measure on the performance was based on the area under the curve (AUC) of Receiver Operating Characteristics. From 284 nodules (122 malignant and 162 benign ones), the validation experiments reported a mean of 0.9051 and standard deviation of 0.0397 for the AUC value on average over 100 randomizations.

Characterization of follicle and CL development in beef heifers using high resolution three-dimensional ultrasonography

Interpreting medical images is certainly a complex task which requires extensive knowledge [1]. According to Computer Aided Diagnosis (CAD) serves as a second opinion that will help radiologists in diagnosis and on the other hand Content-based Image Retrieval uses visual content to help users browse, search and retrieve similar medical images from a database based on the user’s interest [2-4]. The competency of the CBMIR system depends on feature extraction methods [5]. The textural features are very important to determine the content of a medical image. Textural features provide scenic depth, the spatial distribution of tonal variation, and surface orientation [6]. Therefore, this study seeks to compare and evaluate some of the hand-crafted texture feature extraction techniques in CBMIR. This is to help those concerned in enhancing CBIR systems to make informed decisions concerning the selection of the best textural feature extraction techniques. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric when considering which of the techniques to choose for a particular study in CBMIR systems. The objective of this work, therefore, is to comparatively evaluate the performance of the following texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and Features from Accelerated Segment Test and Binary Robust Independent Elementary Features (FAST &BRIEF) using the metrics; precision, recall, f1-score, mean squared error (MSE), accuracy and time. These techniques are coupled with specific similarity measure to obtain results. The results showed that LBP, Haralick Descriptor, FAST, and GLCM had the best results in terms of (Precision and Accuracy), Time, F1-Score, and Recall respectively. LBP had 82.05% and 88.23% scores for precision and accuracy respectively. The following scores represent the performance of the Haralick descriptor, FAST, and GLCM models respectively; 0.88s, 38.7%, and 44.82%. These test scores are obtained from datasets ranging from 1k-10.5k. Aside from LBP outperforming the other 5 models mentioned, it still outperformed the following proposed models ' [7]’, ‘Tamura texture feature and wavelet transform combined with Hausdorff distance - [8]’, ‘ [9]’ in terms of (precision, accuracy, and recall) and (precision and recall) respectively and probably f1-score (since f1-score is the weighted average of precision and recall). It is believed that an ensemble of LBP, Haralick descriptors, and Support Vector Machine (SVM) can represent a robust system for both medical image retrieval and classification.

Pomegranate blackheart disease, as an internal disease affecting the global pomegranate industry, is difficult to identify externally and urgently requires non-destructive detection methods for rapid diagnosis. This study established discriminative models for blackheart disease severity in pomegranates by using near-infrared (NIR) spectroscopy and soft X-ray imaging techniques. The results showed that the optimal NIR-based discriminative model, constructed with a Random Forest (RF) algorithm based on spectra preprocessed by the second-derivative (D2) denoising and a Competitive Adaptive Reweighted Sampling (CARS) algorithm, achieved a prediction set accuracy of 86.00%; the optimal soft X-ray imaging-based discriminative model, built with an RF algorithm using textural features extracted from images preprocessed by median filtering and a Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm combined with gray-level co-occurrence matrix (GLCM) and gray-gradient co-occurrence matrix (GGCM) algorithms, reached a prediction set accuracy of 93.10%. In terms of model performance, the model based on soft X-ray imaging exhibited superior performance. Both techniques possess distinct advantages and limitations yet enable non-destructive detection of pomegranate blackheart disease. Further technical optimizations in the future could provide enhanced support for the healthy development of the pomegranate industry.

Texture feature of the froth image is widely used in the working condition recognition of froth flotation. However, due to the complexity of the froth image, the current texture features vary greatly and are difficult to identify the work condition accurately. Therefore, we propose a dynamic texture feature named LBP on the TOP and GLCM Histograms (LTGH) which integrates the local binary patterns (LBPs) and gray-level co-occurrence matrix (GLCM) histograms on the three orthogonal planes (TOP). First, we use the rotation invariant LBPs to enhance rotation invariance and illumination robustness. Then, we implement the TOP on the enhanced texture feature map to generate the multiple dimensional enhanced feature maps. After that, we calculate the GLCM and supplementary features (SFs) on the multiple dimensional enhanced feature map. Finally, we integrate the histogram of the GLCM and SFs to discriminate the texture feature. The LTGH feature considers the froth structures both in the macrolevel and microlevel and captures the temporal information between the froth images. Experiments have demonstrated the effectiveness and stability of the proposed texture feature for work condition recognition in froth flotation. Compared with other traditional texture features, the accuracy of the LTGH feature has been increased by at least 7.76%.

Plant diseases are one of the main causes to reduce the quality and quantity of crop yield. Traditional expert eye necked opinion method is used to recognize the crop diseases. In rural areas getting experts, opinion is a more tedious job. The automated system is required to recognize the disease accurately and quickly. In automated systems, feature extraction played an important role. Many feature extraction techniques are suggested for the classification of plant diseases. According to the literature, the local binary pattern (LBP) method has good object recognition and classification performance. In our work, we have compared a study of extracted features based on the LBP and gray-level co-occurrence matrix (GLCM) feature with color features for the recognition of banana plant leaf diseases. The experiments were performed using different banana leaf diseases like banana yellow sigatoka, bunchy top, cucumber mosaic virus, and healthy leaves. The result shows that the features extracted using LBP are more accurate as compared to GLCM when compared with the SVM classifier. KNN gives more accurate results with GLCM.

Magnetic Resonance Imaging plays a vital part in diagnosis of mutant cells in the endometrial layer of women reproductive system. This Endometrial carcinoma (EC) is one among the type of uterine cancer acts as a major challenge for the medical practitioners for early diagnosis and classification. This study explores advanced imaging techniques and artificial intelligence (AI) for improved EC identification. The Methodology incudes, an approach of extracting texture features using Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gray Level Co-occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRLM) from the image data. These features are fused using a hybrid approach to capture complementary information. Machine learning classifiers including Random Forest (RF), Radial Basis Function (RBF), Artificial Neural Network (ANN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are trained and evaluated. Results demonstrate significant improvements in classification accuracy, with the hybrid feature extraction method achieving significant accuracy. Explainable AI (XAI) techniques are employed to interpret and visualize classifier decisions, providing details into the discriminative features contributing to EC classification.. The findings support the potential of integrating advanced imaging and machine learning, facilitated by XAI, for precise EC diagnosis and therapeutic planning.