Fusion of the Textural Feature and Palm-Lines for Palmprint Authentication

2005 ◽  
pp. 1075-1084 ◽  
Author(s):  
Xiangqian Wu ◽  
Fengmiao Zhang ◽  
Kuanquan Wang ◽  
David Zhang
Keyword(s):  
Textural Feature

scholarly journals Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 371
Author(s):  
Yu Jin ◽  
Jiawei Guo ◽  
Huichun Ye ◽  
Jinling Zhao ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Feature Space ◽  
Kappa Coefficient ◽  
Classification Model ◽  
Support Vector ◽  
Textural Feature ◽  
Monitoring Accuracy ◽  
Areca Catechu ◽  
High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

Determination of steel quality based on discriminating textural feature selection

2011 ◽  
Vol 66 (23) ◽  
pp. 6264-6271 ◽  
Author(s):  
Daeyoun Kim ◽  
J. Jay Liu ◽  
Chonghun Han
Keyword(s):  
Feature Selection ◽  
Textural Feature ◽  
Steel Quality

Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images

2020 ◽  
pp. 3-14
Author(s):  
Elisabeth Pfaehler ◽  
Liesbet Mesotten ◽  
Gem Kramer ◽  
Michiel Thomeer ◽  
Karolien Vanhove ◽  
...  
Keyword(s):  
Textural Feature ◽  
Segmentation Approach ◽  
Feature Based

Face Skin Disease Detection with Textural Feature Extraction

2020 ◽  
Author(s):  
Wirdayanti ◽  
Irwan Mahmudi ◽  
Andi Chairul Ahsan ◽  
Anita Ahmad Kasim ◽  
Rosmala Nur ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Skin Disease ◽  
Disease Detection ◽  
Textural Feature

scholarly journals MLTI-17. DIFFERENTIATION OF RADIATION INJURY FROM RECURRENT BRAIN METASTASIS USING COMBINED FET PET/MRI RADIOMICS

2019 ◽  
Vol 1 (Supplement_1) ◽  
pp. i17-i18
Author(s):  
Philipp Lohmann ◽  
Martin Kocher ◽  
Garry Ceccon ◽  
Elena Bauer ◽  
Gabriele Stoffels ◽  
...  
Keyword(s):  
Brain Metastasis ◽  
Brain Metastases ◽  
Diagnostic Accuracy ◽  
Radiation Injury ◽  
Imaging Modality ◽  
Feature Analysis ◽  
Textural Features ◽  
Textural Feature ◽  
Fet Pet ◽  
Mri Features

Abstract BACKGROUND: The aim of this study was to investigate the potential of combined radiomics textural feature analysis of contrast-enhanced MRI (CE MRI) and static O-(2-[18F]fluoroethyl)-L-tyrosine(FET) PET for the differentiation of recurrent brain metastasis from radiation injury. PATIENTS AND METHODS: Fifty-two patients with newly diagnosed or progressive contrast-enhancing brain lesions on MRI after radiotherapy (predominantly radiosurgery, 84% of patients) of brain metastases were additionally investigated using FET PET. Based on histology (n=19) or clinicoradiological follow-up (n=33), local recurrent brain metastases were diagnosed in 21 patients (40%) and radiation injury in 31 patients (60%). Forty-two features (shape-based, first and second order features) were calculated on both unfiltered and filtered CE MRI and summed FET PET images (20–40 min p.i). After feature selection, logistic regression models using a maximum of five features to avoid overfitting were calculated for each imaging modality separately and for the combined FET PET/MRI features. The resulting models were validated using cross-validation. Diagnostic accuracies were calculated for each imaging modality separately as well as for the combined model. RESULTS: For differentiation between radiation injury and brain metastasis recurrence, textural features extracted from CE MRI had a diagnostic accuracy of 81%. FET PET textural features revealed a slightly higher diagnostic accuracy of 83%. However, the highest diagnostic accuracy was obtained when combining CE MRI and FET PET features (accuracy, 89%). CONCLUSION: Our findings suggest that combined FET PET/MRI radiomics using textural feature analysis offers a great potential to contribute significantly to the management of patients with brain metastases. SUPPORT: This work was supported by the Wilhelm-Sander Stiftung, Germany

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