scholarly journals Evaluation of Fetal Lung Ultrasound Images by Digital Texture Analysis Methods

2015 ◽  
Vol 06 (Supplement 4) ◽  
Keyword(s):  
Texture Analysis ◽  
Lung Ultrasound ◽  
Fetal Lung ◽  
Ultrasound Images ◽  
Analysis Methods

scholarly journals Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

2018 ◽  
Vol 38 (6) ◽  
pp. 1459-1476
Author(s):  
Alvaro Perez‐Moreno ◽  
Mara Dominguez ◽  
Federico Migliorelli ◽  
Eduard Gratacos ◽  
Montse Palacio ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Quantitative Ultrasound ◽  
Lung Ultrasound ◽  
Fetal Lung ◽  
Ultrasound Images

Prenatal Prediction of Neonatal Respiratory Morbidity: A Radiomics Method Based on Imbalanced Few-Shot Fetal Lung Ultrasound Images

2021 ◽  
Author(s):  
Jing Jiao ◽  
Yanran Du ◽  
Xiaokang Li ◽  
Yi Guo ◽  
Yunyun Ren ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Data Augmentation ◽  
Lung Ultrasound ◽  
Fetal Lung ◽  
Performance Outcomes ◽  
Respiratory Morbidity ◽  
Ultrasound Images ◽  
Evaluation Tool ◽  
Non Invasive ◽  
Fetal Lungs

Abstract Background: To develop a non-invasive method for the prenatal prediction of neonatal respiratory morbidity (NRM) by a novel radiomics method based on imbalanced few-shot fetal lung ultrasound images.Methods: A total of 210 fetal lung ultrasound images were enrolled in this study, including 159 normal newborns and 51 NRM newborns. Fetal lungs were delineated as the region of interest (ROI), where radiomics features were designed and extracted. Integrating radiomics features selected and two clinical features, including gestational age (GA) and gestational diabetes mellitus (GDM), the prediction model was developed and evaluated. The modelling methods used were data augmentation, cost-sensitive learning, and ensemble learning. Furthermore, two methods, which embed data balancing into ensemble learning, were employed to address the problems of imbalance and few-shot simultaneously.Results: Our model achieved sensitivity values of 0.82, specificity values of 0.84, accuracy values of 0.84 and area under the curve values of 0.87 in the test set. The radiomics features extracted from the ROIs at different locations within the lung region achieved similar classification performance outcomes.Conclusion: The feature set we designed can efficiently and robustly describe fetal lungs for NRM prediction. RUSBoost shows excellent performance compared to state-of-the-art classifiers on the imbalanced few-shot dataset. The diagnostic efficacy of the model we developed is similar to that of several previous reports of amniocentesis and can serve as a non-invasive, precise evaluation tool for NRM prediction.

scholarly journals Prenatal prediction of neonatal respiratory morbidity: a radiomics method based on imbalanced few-shot fetal lung ultrasound images

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Jing Jiao ◽  
Yanran Du ◽  
Xiaokang Li ◽  
Yi Guo ◽  
Yunyun Ren ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Data Augmentation ◽  
Lung Ultrasound ◽  
Fetal Lung ◽  
Performance Outcomes ◽  
Respiratory Morbidity ◽  
Ultrasound Images ◽  
Evaluation Tool ◽  
Non Invasive ◽  
Fetal Lungs

Abstract Background To develop a non-invasive method for the prenatal prediction of neonatal respiratory morbidity (NRM) by a novel radiomics method based on imbalanced few-shot fetal lung ultrasound images. Methods A total of 210 fetal lung ultrasound images were enrolled in this study, including 159 normal newborns and 51 NRM newborns. Fetal lungs were delineated as the region of interest (ROI), where radiomics features were designed and extracted. Integrating radiomics features selected and two clinical features, including gestational age and gestational diabetes mellitus, the prediction model was developed and evaluated. The modelling methods used were data augmentation, cost-sensitive learning, and ensemble learning. Furthermore, two methods, which embed data balancing into ensemble learning, were employed to address the problems of imbalance and few-shot simultaneously. Results Our model achieved sensitivity values of 0.82, specificity values of 0.84, balanced accuracy values of 0.83 and area under the curve values of 0.87 in the test set. The radiomics features extracted from the ROIs at different locations within the lung region achieved similar classification performance outcomes. Conclusion The feature set we designed can efficiently and robustly describe fetal lungs for NRM prediction. RUSBoost shows excellent performance compared to state-of-the-art classifiers on the imbalanced few-shot dataset. The diagnostic efficacy of the model we developed is similar to that of several previous reports of amniocentesis and can serve as a non-invasive, precise evaluation tool for NRM prediction.

Wild boar Classification by Using a Fusion of Texture Analysis Methods

2020 ◽  
Author(s):  
Luis C. S. Junior ◽  
Mariceia B. S. Padua ◽  
Leonardo M. Ogusuku ◽  
Marcelo K. Albertini ◽  
Renato Pimentel ◽  
...  
Keyword(s):  
Wild Boar ◽  
Texture Analysis ◽  
Analysis Methods

scholarly journals 950-93 Automatic Identification of Calcified Regions on Intravascular Ultrasound Images of Human Coronary Arteries Using Texture Analysis

1995 ◽  
Vol 25 (2) ◽  
pp. 180A
Author(s):  
Marco Masseroli ◽  
Robert M. Cothren ◽  
E. Murat Tuzcu ◽  
Dominique S. Meier ◽  
James D. Thomas ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Intravascular Ultrasound ◽  
Coronary Arteries ◽  
Automatic Identification ◽  
Ultrasound Images

scholarly journals FORMING THE STACK OF TEXTURE FEATURES FOR LIVER ULTRASOUND IMAGES CLASSIFICATION

2020 ◽  
Vol 3 (4) ◽  
pp. 240-251
Author(s):  
Dmitro Yuriiovych Hrishko ◽  
Ievgen Arnoldovich Nastenko ◽  
Maksym Oleksandrovych Honcharuk ◽  
Volodymyr Anatoliyovich Pavlov
Keyword(s):  
Random Forest ◽  
Texture Analysis ◽  
Binary Classification ◽  
Texture Features ◽  
Normal Liver ◽  
Group Method ◽  
Ultrasound Images ◽  
Liver Ultrasound ◽  
Random Forest Models ◽  
Occurrence Matrix

This article discusses the use of texture analysis methods to obtain informative features that describe the texture of liver ultrasound images. In total, 317 liver ultrasound images were analyzed, which were provided by the Institute of Nuclear Medicine and Radiation Diagnostics of NAMS of Ukraine. The images were taken by three different sensors (convex, linear, and linear sensor in increased signal level mode). Both images of patients with a normal liver condition and patients with specific liver disease (there were diseases such as: autoimmune hepatitis, Wilson's disease, hepatitis B and C, steatosis, and cirrhosis) were present in the database. Texture analysis was used for “Feature Construction”, which resulted in more than a hundred different informative features that made up a common stack. Among them, there are such features as: three authors’ patented features derived from the grey level co-occurrence matrix; features, obtained with the help of spatial sweep method (working by the principle of group method of data handling), which was applied to ultrasound images; statistical features, calculated on the images, brought to one scale with the help of differential horizontal and vertical matrices, which are proposed by the authors; greyscale pairs ensembles (found using the genetic algorithm), which identify liver pathology on images, transformed with the help of horizontal and vertical differentiations, in the best possible way. The resulting trait stack was used to solve the problem of binary classification (“norma-pathology”) of ultrasound liver images. A Machine Learning method, namely “Random Forest”, was used for this purpose. Before the classification, in order to obtain objective results, the total samples were divided into training (70 %), testing (20 %), and examining (10 %). The result was the best three Random Forest models separately for each sensor, which gave the following recognition rates: 93.4 % for the convex sensor, 92.9 % for the linear sensor, and 92 % for the reinforced linear sensor

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