Twenty-two patients with pathologically confirmed glioblastoma who had received concurrent CCRT with TMZ underwent conventional MRI including T1-weighted imaging(T1WI), T2-weighted imaging(T2WI), fluid attenuated inversion recovery(FLAIR)and contrast-enhanced T1WI(T1Ce). Five GLCM texture maps of contrast, energy, entropy, correlation and homogeneity were generated for each MRI series. Of the aforementioned 5 texture features, the most significant features were contrast and correlation on T2WI with areas under ROC curve of 0.883 and 0.892, respectively, and they had the same sensitivity of 75%, specificity of 100%, accuracy of 86.4%, PPV of 100% and NPV of 76.9% in differentiation true progression from pseudoprogression.
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http://dx.doi.org/10.1016/j.clinimag.2015.04.003 | DOI Listing |
Nihon Hoshasen Gijutsu Gakkai Zasshi
January 2025
Department of Radiology, Nara Prefecture General Medical Center.
Purpose: There are attempts to assess tumor heterogeneity by texture analysis. However, the ordered subsets-expectation maximization (OSEM) reconstruction method has problems depicting heterogeneities. The aim of this study was to identify image reconstruction parameters that improve the ability to depict internal tumor necrosis using a self-made phantom that simulates internal necrosis.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany.
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).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Space Robotics Research Group (SpaceR), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg.
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.
View Article and Find Full Text PDFJ Imaging
January 2025
Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia.
Crop field monitoring using unmanned aerial vehicles (UAVs) is one of the most important technologies for plant growth control in modern precision agriculture. One of the important and widely used tasks in field monitoring is plant stand counting. The accurate identification of plants in field images provides estimates of plant number per unit area, detects missing seedlings, and predicts crop yield.
View Article and Find Full Text PDFCarbohydr Polym
March 2025
Plant Fiber Material Science Research Center, State Key Laboratory of Pulp and Paper Engineering, School of Light Industry and Engineering, South China University of Technology, Guangzhou 510640, China; Guangdong Provincial Key Laboratory of Plant Resources Biorefinery, No. 100, West Outer Ring Road, Guangzhou University Town, Panyu District, Guangzhou 510006, China.
Ancient documents and artworks are invaluable cultural heritage artworks that require careful preservation. Traditional methods for assessing their physical and chemical properties-such as tearing index, tensile index, water absorption, and pH-are often destructive, risking irreversible damage. This study introduces a novel, non-destructive approach using Short-Wave Near-Infrared (SWNIR) hyperspectral imaging (HSI) combined with advanced machine learning models.
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