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

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

2D-3D registration of human ankle using X-ray and CT images

2012 ◽  
Author(s):  
Ahmed Shalaby ◽  
Aly A. Farag ◽  
Adrianne Ross ◽  
Todd Hockenbury
Keyword(s):  
Ct Images ◽  
3D Registration ◽  
X Ray ◽  
Human Ankle

A fast rigid-registration method of inferior limb X-ray image and 3D CT images for TKA surgery

2010 ◽  
Author(s):  
Fumihito Ito ◽  
Prima O.D.A ◽  
Ikuko Uwano ◽  
Kenzo Ito
Keyword(s):  
Ct Images ◽  
3D Ct ◽  
Registration Method ◽  
X Ray ◽  
Rigid Registration ◽  
Inferior Limb

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

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

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

Quality Improvement of X-ray CT Images by Estimation of the Mechanical Misalignment in a Projection Image Using an Iterative Method

2017 ◽  
Vol 137 (3) ◽  
pp. 213-219
Author(s):  
Kenji Noguchi ◽  
Satoshi Takahashi ◽  
Daisuke Suzuki ◽  
Atsushi Teramoto
Keyword(s):  
Quality Improvement ◽  
Iterative Method ◽  
Ct Images ◽  
X Ray ◽  
Projection Image

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.

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