A fast registration from 3D CT images to 2D X-ray images

2018 ◽  
Author(s):  
Hanqian Pan ◽  
Chunlin Zhou ◽  
Qi Zhu ◽  
Delei Zheng
Keyword(s):  
Ct Images ◽  
3D Ct ◽  
X Ray

B214 The motion analysis in the patella by simulation of 2D X-ray images based on 3D CT images

2011 ◽  
Vol 2011.22 (0) ◽  
pp. 151-152
Author(s):  
Yoshitaka SHIRAISHI ◽  
Takeshi SHIMOTO ◽  
Hidehiko HIGAKI ◽  
Yoshitaka NAKANISHI ◽  
Yasutaka TASHIRO ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Ct Images ◽  
3D Ct ◽  
X Ray

An approach to 2D/3D registration of a vertebra in 2D x-ray fluoroscopies with 3D CT images

1997 ◽  
pp. 119-128 ◽  
Author(s):  
Jürgen Weese ◽  
Thorsten M. Buzug ◽  
Cristian Lorenz ◽  
Carola Fassnacht
Keyword(s):  
Ct Images ◽  
3D Ct ◽  
3D Registration ◽  
X Ray

scholarly journals Functional Assessment for the Natural Knee Joints in Squat Activity by Simulation of 2D X-Ray Images Based on 3D CT Images

2011 ◽  
Vol 77 (782) ◽  
pp. 3761-3769 ◽  
Author(s):  
Yoshitaka SHIRAISHI ◽  
Takeshi SHIMOTO ◽  
Hidehiko HIGAKI ◽  
Yoshitaka NAKANISHI ◽  
Yasutaka TASHIRO ◽  
...  
Keyword(s):  
Functional Assessment ◽  
Ct Images ◽  
Knee Joints ◽  
3D Ct ◽  
X Ray

Reliability analysis of Cobb angle measurements of congenital scoliosis using X-ray and 3D-CT images

2015 ◽  
Vol 26 (1) ◽  
pp. 53-57 ◽  
Author(s):  
Ryoji Tauchi ◽  
Taichi Tsuji ◽  
Patrick J. Cahill ◽  
John M. Flynn ◽  
John M. Flynn ◽  
...  
Keyword(s):  
Cobb Angle ◽  
Reliability Analysis ◽  
Congenital Scoliosis ◽  
Ct Images ◽  
3D Ct ◽  
X Ray ◽  
Angle Measurements

A framework for pulmonary fissure segmentation in 3D CT images using a directional derivative of plate filter

2020 ◽  
Vol 173 ◽  
pp. 107602 ◽  
Author(s):  
Hong Zhao ◽  
Berend C. Stoel ◽  
Marius Staring ◽  
M. Bakker ◽  
Jan Stolk ◽  
...  
Keyword(s):  
Directional Derivative ◽  
Ct Images ◽  
3D Ct ◽  
Fissure Segmentation ◽  
Plate Filter

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

scholarly journals Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images

2021 ◽  
Vol 11 (9) ◽  
pp. 4233
Author(s):  
Biprodip Pal ◽  
Debashis Gupta ◽  
Md. Rashed-Al-Mahfuz ◽  
Salem A. Alyami ◽  
Mohammad Ali Moni
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Chest Radiography ◽  
High Sensitivity ◽  
Ct Images ◽  
Classification Performance ◽  
Learning Models ◽  
X Ray ◽  
Image Capturing ◽  
Fast Gradient

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

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