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Search Results (239)

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Keywords = gray level co-occurrence matrix (GLCM)

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18 pages, 511 KiB  
Systematic Review
Texture Analysis in Musculoskeletal Ultrasonography: A Systematic Review
by Yih-Kuen Jan, Isabella Yu-Ju Hung and W. Catherine Cheung
Diagnostics 2025, 15(5), 524; https://doi.org/10.3390/diagnostics15050524 - 21 Feb 2025
Viewed by 198
Abstract
Background: The objective of this systematic review was to summarize the findings of texture analyses of musculoskeletal ultrasound images and synthesize the information to facilitate the use of texture analysis on assessing skeletal muscle quality in various pathophysiological conditions. Methods: Medline, PubMed, Scopus, [...] Read more.
Background: The objective of this systematic review was to summarize the findings of texture analyses of musculoskeletal ultrasound images and synthesize the information to facilitate the use of texture analysis on assessing skeletal muscle quality in various pathophysiological conditions. Methods: Medline, PubMed, Scopus, Web of Science, and Cochrane databases were searched from their inception until January 2025 using the PRISMA Diagnostic Test Accuracy and was registered at PROSPERO CRD42025636613. Information related to patients, interventions, ultrasound settings, texture analyses, muscles, and findings were extracted. The quality of evidence was evaluated using QUADAS-2. Results: A total of 38 studies using second-order and higher-order texture analysis met the criteria. The results indicated that no studies used an established reference standard (histopathology) to evaluate the accuracy of ultrasound texture analysis in diagnosing muscle quality. Alternative reference standards were compared, including various physiological, pathological, and pre–post intervention comparisons using over 200+ texture features of various muscles on diverse pathophysiological conditions. Conclusions: The findings of these included studies demonstrating that ultrasound texture analysis was able to discriminate changes in muscle quality using texture analysis between patients with pathological conditions and healthy conditions, including popular gray-level co-occurrence matrix (GLCM)-based contrast, correlation, energy, entropy, and homogeneity. Studies also demonstrated that texture analysis can discriminate muscle quality in various muscles under pathophysiological conditions although evidence is low because of bias in subject recruitment and lack of comparison with the established reference standard. This is the first systematic review of the use of texture analysis of musculoskeletal ultrasonography in assessing muscle quality in various muscles under diverse pathophysiological conditions. Full article
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27 pages, 10700 KiB  
Article
Rice Yield Prediction Using Spectral and Textural Indices Derived from UAV Imagery and Machine Learning Models in Lambayeque, Peru
by Javier Quille-Mamani, Lia Ramos-Fernández, José Huanuqueño-Murillo, David Quispe-Tito, Lena Cruz-Villacorta, Edwin Pino-Vargas, Lisveth Flores del Pino, Elizabeth Heros-Aguilar and Luis Ángel Ruiz
Remote Sens. 2025, 17(4), 632; https://doi.org/10.3390/rs17040632 - 12 Feb 2025
Viewed by 628
Abstract
Predicting rice yield accurately is crucial for enhancing farming practices and securing food supplies. This research aims to estimate rice yield in Peru’s Lambayeque region by utilizing spectral and textural indices derived from unmanned aerial vehicle (UAV) imagery, which offers a cost-effective alternative [...] Read more.
Predicting rice yield accurately is crucial for enhancing farming practices and securing food supplies. This research aims to estimate rice yield in Peru’s Lambayeque region by utilizing spectral and textural indices derived from unmanned aerial vehicle (UAV) imagery, which offers a cost-effective alternative to traditional approaches. UAV data collection in commercial areas involved seven flights in 2022 and ten in 2023, focusing on key growth stages such as flowering, milk, and dough, each showing significant predictive capability. Vegetation indices like NDVI, SP, DVI, NDRE, GNDVI, and EVI2, along with textural features from the gray-level co-occurrence matrix (GLCM) such as ENE, ENT, COR, IDM, CON, SA, and VAR, were combined to form a comprehensive dataset for model training. Among the machine learning models tested, including Multiple Linear Regression (MLR), Support Vector Machines (SVR), and Random Forest (RF), MLR demonstrated high reliability for annual data with an R2 of 0.69 during the flowering and milk stages, and an R2 of 0.78 for the dough stage in 2022. The RF model excelled in the combined analysis of 2022–2023 data, achieving an R2 of 0.58 for the dough stage, all confirmed through cross-validation. Integrating spectral and textural data from UAV imagery enhances early yield prediction, aiding precision agriculture and informed decision-making in rice management. These results emphasize the need to incorporate climate variables to refine predictions under diverse environmental conditions, offering a scalable solution to improve agricultural management and market planning. Full article
(This article belongs to the Special Issue Perspectives of Remote Sensing for Precision Agriculture)
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13 pages, 3805 KiB  
Article
Radiomics-Driven CBCT Texture Analysis as a Novel Biosensor for Quantifying Periapical Bone Healing: A Comparative Study of Intracanal Medications
by Diana Lorena Garcia Lopes, Sérgio Lúcio Pereira de Castro Lopes, Daniela Maria de Toledo Ungaro, Ana Paula Martins Gomes, Nicole Berton de Moura, Bianca Costa Gonçalves and Andre Luiz Ferreira Costa
Biosensors 2025, 15(2), 98; https://doi.org/10.3390/bios15020098 - 9 Feb 2025
Viewed by 620
Abstract
This study aimed to evaluate the effectiveness of two intracanal medications in promoting periapical bone healing following endodontic treatment using radiomics-enabled texture analysis of cone-beam computed tomography (CBCT) images as a novel biosensing technique. By quantifying tissue changes through advanced image analysis, this [...] Read more.
This study aimed to evaluate the effectiveness of two intracanal medications in promoting periapical bone healing following endodontic treatment using radiomics-enabled texture analysis of cone-beam computed tomography (CBCT) images as a novel biosensing technique. By quantifying tissue changes through advanced image analysis, this approach seeks to enhance the monitoring and assessment of endodontic treatment outcomes. Thirty-four single-rooted teeth with pulp necrosis and periapical lesions were allocated to two groups (17 each): calcium hydroxide +2% chlorhexidine gel (CHX) and Ultracal XS®. CBCT scans were obtained immediately after treatment and three months later. Texture analysis performed using MaZda software extracted 11 parameters based on the gray level co-occurrence matrix (GLCM) across two inter-pixel distances and four directions. Statistical analysis revealed significant differences between medications for S [0,1] inverse difference moment (p = 0.043), S [0,2] difference of variance (p = 0.014), and S [0,2] difference of entropy (p = 0.004). CHX treatment resulted in a more organized bone tissue structure post-treatment, evidenced by reduced entropy and variance parameters, while Ultracal exhibited less homogeneity, indicative of fibrous or immature tissue formation. These findings demonstrate the superior efficacy of CHX in promoting bone healing and underscore the potential of texture analysis as a powerful tool for assessing CBCT images in endodontic research. Full article
(This article belongs to the Special Issue Biosensors for Biomedical Diagnostics)
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27 pages, 4940 KiB  
Article
Alzheimer’s Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network
by Raheleh Ghadami and Javad Rahebi
Diagnostics 2025, 15(3), 377; https://doi.org/10.3390/diagnostics15030377 - 5 Feb 2025
Viewed by 499
Abstract
Background/Objective: Alzheimer’s disease is a progressive brain syndrome causing cognitive decline and, ultimately, death. Early diagnosis is essential for timely medical intervention, with MRI medical imaging serving as a primary diagnostic tool. Machine learning (ML) and deep learning (DL) methods are increasingly [...] Read more.
Background/Objective: Alzheimer’s disease is a progressive brain syndrome causing cognitive decline and, ultimately, death. Early diagnosis is essential for timely medical intervention, with MRI medical imaging serving as a primary diagnostic tool. Machine learning (ML) and deep learning (DL) methods are increasingly utilized to analyze these images, but accurately distinguishing between healthy and diseased states remains a challenge. This study aims to address these limitations by developing an integrated approach combining swarm intelligence with ML and DL techniques for Alzheimer’s disease classification. Method: This proposal methodology involves sourcing Alzheimer’s disease-related MRI images and extracting features using convolutional neural networks (CNNs) and the Gray Level Co-occurrence Matrix (GLCM). The Harris Hawks Optimization (HHO) algorithm is applied to select the most significant features. The selected features are used to train a multi-layer perceptron (MLP) neural network and further processed using a long short-term (LSTM) memory network in order to classify tumors as malignant or benign. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset is utilized for assessment. Results: The proposed method achieved a classification accuracy of 97.59%, sensitivity of 97.41%, and precision of 97.25%, outperforming other models, including VGG16, GLCM, and ResNet-50, in diagnosing Alzheimer’s disease. Conclusions: The results demonstrate the efficacy of the proposed approach in enhancing Alzheimer’s disease diagnosis through improved feature extraction and selection techniques. These findings highlight the potential for advanced ML and DL integration to improve diagnostic tools in medical imaging applications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis)
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19 pages, 4734 KiB  
Article
Fractal Analysis of Volcanic Rock Image Based on Difference Box-Counting Dimension and Gray-Level Co-Occurrence Matrix: A Case Study in the Liaohe Basin, China
by Sijia Li, Zhuwen Wang and Dan Mou
Fractal Fract. 2025, 9(2), 99; https://doi.org/10.3390/fractalfract9020099 - 4 Feb 2025
Viewed by 572
Abstract
Volcanic rocks, as a widely distributed rock type on the earth, are mostly buried deep within basins, and their internal structures possess characteristics by irregularity and self-similarity. In the study of volcanic rocks, accurately identifying the lithology of volcanic rocks is significant for [...] Read more.
Volcanic rocks, as a widely distributed rock type on the earth, are mostly buried deep within basins, and their internal structures possess characteristics by irregularity and self-similarity. In the study of volcanic rocks, accurately identifying the lithology of volcanic rocks is significant for reservoir description and reservoir evaluation. The accuracy of lithology identification can improve the success rate of petroleum exploration and development as well as the safety of engineering construction. In this study, we took the electron microscope images of four types of volcanic rocks in the Liaohe Basin as the research objects and comprehensively used the differential box-counting dimension (DBC) and the gray-level co-occurrence matrix (GLCM) to identify the lithology of volcanic rocks. Obtain the images of volcanic rocks in the research area and conduct preprocessing so that the images can meet the requirements of calculations. Firstly, calculate the different box-counting dimension. Divide the grayscale image into boxes of different scales and determine the differential box-counting dimension based on the variation of grayscale values within each box. The differential box-counting dimension of basalt ranges from 1.7 to 1.75, that of trachyte ranges from 1.82 to 1.87, that of gabbro ranges from 1.76 to 1.79, and that of diabase ranges from 1.78 to 1.82. Then, the gray-level co-occurrence matrix is utilized to extract four image texture features of volcanic rock images, namely contrast, energy, entropy, and variance. The recognition of four types of volcanic rock images is achieved by combining the different box-counting dimension and the gray-level co-occurrence matrix. This method has been experimentally verified by volcanic rock image samples. It has a relatively high accuracy in identifying the lithology of volcanic rocks and can effectively distinguish four different types of volcanic rocks. Compared with single-feature recognition methods, this approach significantly improves recognition accuracy, offers reliable technical support and a data basis for volcanic rock-related geological analyses, and drives the further development of volcanic rock research. Full article
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36 pages, 13780 KiB  
Article
Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines
by Ronald P. Dillner, Maria A. Wimmer, Matthias Porten, Thomas Udelhoven and Rebecca Retzlaff
Sensors 2025, 25(2), 431; https://doi.org/10.3390/s25020431 - 13 Jan 2025
Viewed by 729
Abstract
Assessing vines’ vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely [...] Read more.
Assessing vines’ vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features). The specific features were selected based on extensive literature research, including especially the fields of precision agri- and viticulture. To integrate only vine canopy-exclusive features into ML classifications, different feature types were extracted and spatially aggregated (zonal statistics), based on a combined pixel- and object-based image-segmentation-technique-created vine row mask around each single grapevine position. The extracted canopy features were progressively grouped into seven input feature groups for model training. Model overall performance metrics were optimized with grid search-based hyperparameter tuning and repeated-k-fold-cross-validation. Finally, ML-based growth class prediction results were extensively discussed and evaluated for overall (accuracy, f1-weighted) and growth class specific- classification metrics (accuracy, user- and producer accuracy). Full article
(This article belongs to the Special Issue Remote Sensing for Crop Growth Monitoring)
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18 pages, 2256 KiB  
Article
Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears
by Jhonathan Sora-Cardenas, Wendy M. Fong-Amaris, Cesar A. Salazar-Centeno, Alejandro Castañeda, Oscar D. Martínez-Bernal, Daniel R. Suárez and Carol Martínez
Sensors 2025, 25(2), 390; https://doi.org/10.3390/s25020390 - 10 Jan 2025
Viewed by 871
Abstract
Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. [...] Read more.
Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification. Using a dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins and texture analysis with the gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), for image quality assessment. Leukocyte detection employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, and erosion, followed by the connected components algorithm. Parasite detection used high-intensity region selection and adaptive bounding boxes, followed by a custom convolutional neural network (CNN) for candidate identification. A second CNN classified parasites into trophozoites, schizonts, and gametocytes. The system achieved an F1-score of 95% for image quality evaluation, 88.92% for leukocyte detection, and 82.10% for parasite detection. The F1-score—a metric balancing precision (correctly identified positives) and recall (correctly detected instances out of actual positives)—is especially valuable for assessing models on imbalanced datasets. In parasite stage classification, CNN achieved F1-scores of 85% for trophozoites, 88% for schizonts, and 83% for gametocytes. This study introduces a robust and scalable automated system that addresses critical challenges in malaria diagnosis by integrating advanced image quality assessment and deep learning techniques for parasite detection and classification. This system’s adaptability to low-resource settings underscores its potential to improve malaria diagnostics globally. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Imaging Sensors and Processing)
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20 pages, 4706 KiB  
Article
Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering
by Junbin Zhuang, Wenying Chen, Xunan Huang and Yunyi Yan
Remote Sens. 2025, 17(2), 193; https://doi.org/10.3390/rs17020193 - 8 Jan 2025
Viewed by 531
Abstract
Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification [...] Read more.
Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability. Full article
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11 pages, 4367 KiB  
Article
Gray-Level Co-Occurrence Matrix Uniformity Correction Algorithm in Positron Emission Tomographic Image: A Phantom Study
by Kyuseok Kim and Youngjin Lee
Photonics 2025, 12(1), 33; https://doi.org/10.3390/photonics12010033 - 3 Jan 2025
Viewed by 536
Abstract
High uniformity of positron emission tomography (PET) images in the field of nuclear medicine is necessary to obtain excellent and stable data from the system. In this study, we aimed to apply and optimize a PET/magnetic resonance (MR) imaging system by approaching the [...] Read more.
High uniformity of positron emission tomography (PET) images in the field of nuclear medicine is necessary to obtain excellent and stable data from the system. In this study, we aimed to apply and optimize a PET/magnetic resonance (MR) imaging system by approaching the gray-level co-occurrence matrix (GLCM), which is known to be efficient in the uniformity correction of images. CAIPIRINHA Dixon-VIBE was used as an MR image acquisition pulse sequence for the fast and accurate attenuation correction of PET images, and the phantom was constructed by injecting NaCl and NaCl + NiSO4 solutions. The lambda value of the GLCM algorithm for uniformity correction of the acquired PET images was optimized in terms of energy and contrast. By applying the GLCM algorithm optimized in terms of energy and contrast to the PET images of phantoms using NaCl and NaCl + NiSO4 solutions, average percent image uniformity (PIU) values of 26.01 and 83.76 were derived, respectively. Compared to the original PET image, an improved PIU value of more than 30% was derived from the PET image to which the proposed optimized GLCM algorithm was applied. In conclusion, we demonstrated that an algorithm optimized in terms of the GLCM energy and contrast can improve the uniformity of PET images. Full article
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16 pages, 4152 KiB  
Article
Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits
by Peng Chen, Xutong Shao, Guangyu Wen, Yaowu Song, Rao Fu, Xiaoyan Xiao, Tulin Lu, Peina Zhou, Qiaosheng Guo, Hongzhuan Shi and Chenghao Fei
Foods 2025, 14(1), 5; https://doi.org/10.3390/foods14010005 - 24 Dec 2024
Viewed by 768
Abstract
The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI [...] Read more.
The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law’s texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire–ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm’s effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, p < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity. Full article
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14 pages, 1342 KiB  
Article
Diffusion-Weighted MRI and Human Papillomavirus (HPV) Status in Oropharyngeal Cancer
by Heleen Bollen, Rüveyda Dok, Frederik De Keyzer, Sarah Deschuymer, Annouschka Laenen, Johannes Devos, Vincent Vandecaveye and Sandra Nuyts
Cancers 2024, 16(24), 4284; https://doi.org/10.3390/cancers16244284 - 23 Dec 2024
Viewed by 762
Abstract
Background: This study aimed to explore the differences in quantitative diffusion-weighted (DW) MRI parameters in oropharyngeal squamous cell carcinoma (OPC) based on Human Papillomavirus (HPV) status before and during radiotherapy (RT). Methods: Echo planar DW sequences acquired before and during (chemo)radiotherapy (CRT) of [...] Read more.
Background: This study aimed to explore the differences in quantitative diffusion-weighted (DW) MRI parameters in oropharyngeal squamous cell carcinoma (OPC) based on Human Papillomavirus (HPV) status before and during radiotherapy (RT). Methods: Echo planar DW sequences acquired before and during (chemo)radiotherapy (CRT) of 178 patients with histologically proven OPC were prospectively analyzed. The volumetric region of interest (ROI) was manually drawn on the apparent diffusion coefficient (ADC) map, and 105 DW-MRI radiomic parameters were extracted. Change in ADC values (Δ ADC) was calculated as the difference between baseline and during RT at week 4, normalized by the baseline values. Results: Pre-treatment first-order 10th percentile ADC and Gray Level co-occurrence matrix (GLCM)-correlation were significantly lower in HPV-positive compared with HPV-negative tumors (82.4 × 10−5 mm2/s vs. 90.3 × 10−5 mm2/s, p = 0.03 and 0.18 vs. 0.30, p < 0.01). In the fourth week of RT, all first-order ADC values were significantly higher in HPV-positive tumors (p < 0.01). Δ ADC mean was significantly higher for the HPV-positive compared with the HPV-negative OPC group (95% vs. 55%, p < 0.01). A predictive model for HPV status based on smoking status, alcohol consumption, GLCM correlation, and mean ADC and 10th percentile ADC values yielded an area under the curve of 0.77 (95% CI 0.70–0.84). Conclusions: Our results highlight the potential of DW-MR imaging as a non-invasive biomarker for the prediction of HPV status, although its current role remains supplementary to pathological confirmation. Full article
(This article belongs to the Special Issue Advances in Radiotherapy for Head and Neck Cancer)
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18 pages, 2563 KiB  
Article
Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces
by Kacoutchy Jean Ayikpa, Abou Bakary Ballo, Diarra Mamadou and Pierre Gouton
J. Imaging 2024, 10(12), 327; https://doi.org/10.3390/jimaging10120327 - 18 Dec 2024
Viewed by 876
Abstract
Determining the maturity of cocoa pods early is not just about guaranteeing harvest quality and optimizing yield. It is also about efficient resource management. Rapid identification of the stage of maturity helps avoid losses linked to a premature or late harvest, improving productivity. [...] Read more.
Determining the maturity of cocoa pods early is not just about guaranteeing harvest quality and optimizing yield. It is also about efficient resource management. Rapid identification of the stage of maturity helps avoid losses linked to a premature or late harvest, improving productivity. Early determination of cocoa pod maturity ensures both the quality and quantity of the harvest, as immature or overripe pods cannot produce premium cocoa beans. Our innovative research harnesses artificial intelligence and computer vision technologies to revolutionize the cocoa industry, offering precise and advanced tools for accurately assessing cocoa pod maturity. Providing an objective and rapid assessment enables farmers to make informed decisions about the optimal time to harvest, helping to maximize the yield of their plantations. Furthermore, by automating this process, these technologies reduce the margins for human error and improve the management of agricultural resources. With this in mind, our study proposes to exploit a computer vision method based on the GLCM (gray level co-occurrence matrix) algorithm to extract the characteristics of images in the RGB (red, green, blue) and LAB (luminance, axis between red and green, axis between yellow and blue) color spaces. This approach allows for in-depth image analysis, which is essential for capturing the nuances of cocoa pod maturity. Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. The results demonstrated that the combination of algorithms produced superior performance, especially in the LAB color space, where voting scored 98.49% and stacking 98.71%. In comparison, in the RGB color space, voting scored 96.59% and stacking 97.06%. These results surpass those generally reported in the literature, showing the increased effectiveness of combined approaches in improving the accuracy of classification models. This highlights the importance of exploring ensemble techniques to maximize performance in complex contexts such as cocoa pod maturity classification. Full article
(This article belongs to the Special Issue Imaging Applications in Agriculture)
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19 pages, 1818 KiB  
Article
Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models
by Towfeeq Fairooz, Sara E. McNamee, Dewar Finlay, Kok Yew Ng and James McLaughlin
Biosensors 2024, 14(12), 611; https://doi.org/10.3390/bios14120611 - 13 Dec 2024
Viewed by 962
Abstract
Lateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artificial intelligence models. A [...] Read more.
Lateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artificial intelligence models. A modified Gray-Level Co-occurrence Matrix, termed the Averaged Horizontal Multiple Offsets Gray-Level Co-occurrence Matrix, was utilised to compute the textural features of the biosensor assay images. Significant textural features were selected for further analysis. A deep learning Convolutional Neural Network model was employed to extract features from these textural features. Both traditional machine learning models and hybrid artificial intelligence models, which combine Convolutional Neural Network features with traditional algorithms, were used to categorise these textural features based on the thyroid-stimulating hormone concentration levels. The proposed method achieved accuracy levels exceeding 95%. This pioneering study highlights the utility of textural aspects of assay images for accurate predictive disease modelling, offering promising advancements in diagnostics and management within biomedical research. Full article
(This article belongs to the Special Issue Biosensing Advances in Lateral Flow Assays (LFA))
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32 pages, 22123 KiB  
Article
Automated Seedling Contour Determination and Segmentation Using Support Vector Machine and Image Features
by Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Kyu-Ho Lee, Md Asrakul Haque, Md Razob Ali, Yeon Jin Cho, Dong Hee Noh and Sun-Ok Chung
Agronomy 2024, 14(12), 2940; https://doi.org/10.3390/agronomy14122940 - 10 Dec 2024
Cited by 1 | Viewed by 811
Abstract
Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors [...] Read more.
Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R2 up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity. Full article
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36 pages, 41599 KiB  
Article
A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery
by John B. Kilbride and Robert E. Kennedy
Remote Sens. 2024, 16(23), 4586; https://doi.org/10.3390/rs16234586 - 6 Dec 2024
Viewed by 785
Abstract
Aboveground biomass (AGB) estimates derived from Landsat’s spectral bands are limited by spectral saturation when AGB densities exceed 150–300 Mg ha1. Statistical features that characterize image texture have been proposed as a means to alleviate spectral saturation. However, apart from [...] Read more.
Aboveground biomass (AGB) estimates derived from Landsat’s spectral bands are limited by spectral saturation when AGB densities exceed 150–300 Mg ha1. Statistical features that characterize image texture have been proposed as a means to alleviate spectral saturation. However, apart from Gray Level Co-occurrence Matrix (GLCM) statistics, many spatial feature engineering techniques (e.g., morphological operations or edge detectors) have not been evaluated in the context of forest AGB estimation. Moreover, many prior investigations have been constrained by limited geographic domains and sample sizes. We utilize 176 lidar-derived AGB maps covering ∼9.3 million ha of forests in the Pacific Northwest of the United States to construct an expansive AGB modeling dataset that spans numerous biophysical gradients and contains AGB densities exceeding 1000 Mg ha1. We conduct a large-scale inter-comparison of multiple spatial feature engineering techniques, including GLCMs, edge detectors, morphological operations, spatial buffers, neighborhood vectorization, and neighborhood similarity features. Our numerical experiments indicate that statistical features derived from GLCMs and spatial buffers yield the greatest improvement in AGB model performance out of the spatial feature engineering strategies considered. Including spatial features in Random Forest AGB models reduces the root mean squared error (RMSE) by 9.97 Mg ha1. We contextualize this improvement model performance by comparing to AGB models developed with multi-temporal features derived from the LandTrendr and Continuous Change Detection and Classification algorithms. The inclusion of temporal features reduces the model RMSE by 18.41 Mg ha1. When spatial and temporal features are both included in the model’s feature set, the RMSE decreases by 21.71 Mg ha1. We conclude that spatial feature engineering strategies can yield nominal gains in model performance. However, this improvement came at the cost of increased model prediction bias. Full article
(This article belongs to the Section Biogeosciences Remote Sensing)
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