Identification and filtering of rainfall and ground radar echoes using textural features

Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.

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Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma

Scientific Reports ◽

10.1038/s41598-021-86497-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yong Zhu ◽

Yingfan Mao ◽

Jun Chen ◽

Yudong Qiu ◽

Yue Guan ◽

...

Keyword(s):

Texture Analysis ◽

Ct Images ◽

Textural Features ◽

Mutation Status ◽

Portal Venous Phase ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Venous Phase ◽

Ct Texture Analysis

AbstractTo explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

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Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9412099 ◽

2021 ◽

Author(s):

Giulia Orru ◽

Marco Micheletto ◽

Fabio Terranova ◽

Gian Luca Marcialis

Keyword(s):

Signal Processing ◽

Brain Computer Interface ◽

Computer Interface ◽

Textural Features

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Investigation of Polar Mesospheric Summer Echoes Using Linear Discriminant Analysis

Remote Sensing ◽

10.3390/rs13030522 ◽

2021 ◽

Vol 13 (3) ◽

pp. 522

Author(s):

Dorota Jozwicki ◽

Puneet Sharma ◽

Ingrid Mann

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Image Data ◽

Atmospheric Conditions ◽

Very High Frequency ◽

Vhf Radar ◽

Linear Discriminant ◽

Radar Echoes ◽

Summer Echoes ◽

Percent Accuracy

Polar Mesospheric Summer Echoes (PMSE) are distinct radar echoes from the Earth’s upper atmosphere between 80 to 90 km altitude that form in layers typically extending only a few km in altitude and often with a wavy structure. The structure is linked to the formation process, which at present is not yet fully understood. Image analysis of PMSE data can help carry out systematic studies to characterize PMSE during different ionospheric and atmospheric conditions. In this paper, we analyze PMSE observations recorded using the European Incoherent SCATter (EISCAT) Very High Frequency (VHF) radar. The collected data comprises of 18 observations from different days. In our analysis, the image data is divided into regions of a fixed size and grouped into three categories: PMSE, ionosphere, and noise. We use statistical features from the image regions and employ Linear Discriminant Analysis (LDA) for classification. Our results suggest that PMSE regions can be distinguished from ionosphere and noise with around 98 percent accuracy.

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Discriminative Textural Features for Image and Video Colorization

IEICE Transactions on Information and Systems ◽

10.1587/transinf.e95.d.1722 ◽

2012 ◽

Vol E95.D (7) ◽

pp. 1722-1730 ◽

Cited By ~ 8

Author(s):

Michal KAWULOK ◽

Jolanta KAWULOK ◽

Bogdan SMOLKA

Keyword(s):

Textural Features

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