scholarly journals Numerical estimation of rock properties and textural facies classification of core samples using X-Ray Computed Tomography images

2017 ◽  
Vol 41 ◽  
pp. 562-581 ◽  
Author(s):  
Mohamed Soufiane Jouini ◽  
Noomane Keskes
Keyword(s):  
Computed Tomography ◽  
Rock Properties ◽  
Numerical Estimation ◽  
X Ray ◽  
Core Samples ◽  
Facies Classification ◽  
Computed Tomography Images ◽  
X Ray Computed

scholarly journals A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on Cancer

2021 ◽  
Vol 9 (B) ◽  
pp. 1283-1289
Author(s):  
Jane Aurelia ◽  
Zuherman Rustam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computed Tomography ◽  
Support Vector Machine ◽  
Support Vector ◽  
X Ray ◽  
Network Support ◽  
Computed Tomography Images ◽  
X Ray Computed

BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory. AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM. METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer. RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value. CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.

scholarly journals Visualization Ability of Phase-Contrast Synchrotron-Based X-Ray Imaging Using an X-Ray Interferometer in Soft Tissue Tumors

2021 ◽  
Vol 20 ◽  
pp. 153303382110101
Author(s):  
Thet-Thet Lwin ◽  
Akio Yoneyama ◽  
Hiroko Maruyama ◽  
Tohoru Takeda
Keyword(s):  
Computed Tomography ◽  
Soft Tissue ◽  
Spatial Resolution ◽  
Phase Contrast ◽  
Soft Tissue Tumors ◽  
X Ray ◽  
3 Dimensional ◽  
Computed Tomography Images ◽  
X Ray Imaging ◽  
X Ray Computed

Phase-contrast synchrotron-based X-ray imaging using an X-ray interferometer provides high sensitivity and high spatial resolution, and it has the ability to depict the fine morphological structures of biological soft tissues, including tumors. In this study, we quantitatively compared phase-contrast synchrotron-based X-ray computed tomography images and images of histopathological hematoxylin-eosin-stained sections of spontaneously occurring rat testicular tumors that contained different types of cells. The absolute densities measured on the phase-contrast synchrotron-based X-ray computed tomography images correlated well with the densities of the nuclear chromatin in the histological images, thereby demonstrating the ability of phase-contrast synchrotron-based X-ray imaging using an X-ray interferometer to reliably identify the characteristics of cancer cells within solid soft tissue tumors. In addition, 3-dimensional synchrotron-based phase-contrast X-ray computed tomography enables screening for different structures within tumors, such as solid, cystic, and fibrous tissues, and blood clots, from any direction and with a spatial resolution down to 26 μm. Thus, phase-contrast synchrotron-based X-ray imaging using an X-ray interferometer shows potential for being useful in preclinical cancer research by providing the ability to depict the characteristics of tumor cells and by offering 3-dimensional information capabilities.

scholarly journals Computation of permeability of a non-crimp carbon textile reinforcement based on X-ray computed tomography images

2016 ◽  
Vol 81 ◽  
pp. 289-295 ◽  
Author(s):  
Ilya Straumit ◽  
Christoph Hahn ◽  
Elisabeth Winterstein ◽  
Bernhard Plank ◽  
Stepan V. Lomov ◽  
...  
Keyword(s):  
Computed Tomography ◽  
X Ray ◽  
Computed Tomography Images ◽  
X Ray Computed

Automatic Knot Detection in Coarse-Resolution Cone-Beam Computed Tomography Images of Softwood Logs

2019 ◽  
Vol 69 (3) ◽  
pp. 185-187
Author(s):  
Magnus Fredriksson ◽  
Julie Cool ◽  
Stavros Avramidis
Keyword(s):  
Computed Tomography ◽  
Exploratory Study ◽  
Ct Scanning ◽  
Detection Algorithm ◽  
Cone Beam ◽  
X Ray ◽  
Coarse Resolution ◽  
Computed Tomography Images ◽  
X Ray Computed ◽  
Accurate Position

Abstract X-ray computed tomography (CT) scanning of sawmill logs is associated with costly and complex machines. An alternative scanning solution was developed, but its data have not been evaluated regarding detection of internal features. In this exploratory study, a knot detection algorithm was applied to images of four logs to evaluate its performance in terms of knot position and size. The results were a detection rate of 67 percent, accurate position, and inaccurate size. Although the sample size was small, it was concluded that automatic knot detection in coarse resolution CT images of softwoods is feasible, albeit for knots of sufficient size.

scholarly journals Segmentation of multi-phase X-ray computed tomography images

2015 ◽  
Vol 2 (2) ◽  
pp. 104-117 ◽  
Author(s):  
Masaji Kato ◽  
Manabu Takahashi ◽  
Satoru Kawasaki ◽  
Katsuhiko Kaneko
Keyword(s):  
Computed Tomography ◽  
X Ray ◽  
Computed Tomography Images ◽  
X Ray Computed ◽  
Multi Phase

scholarly journals A New Validation Methodology for In Silico Tools Based on X-ray Computed Tomography Images of Tablets and a Performance Analysis of One Tool

Pharmaceutics ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1488
Author(s):  
Sebastian Bollmann ◽  
Peter Kleinebudde
Keyword(s):  
Computed Tomography ◽  
In Silico ◽  
Pharmaceutical Formulations ◽  
Prediction Errors ◽  
Micro Computed Tomography ◽  
Pharmaceutical Dosage Forms ◽  
X Ray ◽  
Computed Tomography Images ◽  
X Ray Computed ◽  
In Silico Tools

In silico tools which predict the dissolution of pharmaceutical dosage forms using virtual matrices can be validated with virtual matrices based on X-ray micro-computed tomography images of real pharmaceutical formulations. Final processed images of 3 different tablet batches were used to check the performance of the in silico tool F-CAD. The goal of this work was to prove the performance of the software by comparing the predicted dissolution profiles to the experimental ones and to check the feasibility and application of the validation concept for in silico tools. Both virtual matrices based on X-ray micro-computed tomography images and designed by the software itself were used. The resulting dissolution curves were compared regarding their similarity to the experimental curve. The kinetics were analysed with the Higuchi and Korsmeyers–Peppas plot. The whole validation concept as such was feasible and worked well. It was possible to identify prediction errors of the software F-CAD and issues with the virtual tablets designed within the software.

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