Non-invasive classification of breast microcalcifications using x-ray coherent scatter computed tomography

2017 ◽  
Vol 62 (3) ◽  
pp. 1192-1207 ◽  
Author(s):  
Bahaa Ghammraoui ◽  
Lucretiu M Popescu
Keyword(s):  
Computed Tomography ◽  
Coherent Scatter ◽  
X Ray ◽  
Non Invasive

X-ray microtomography

1988 ◽  
Vol 46 ◽  
pp. 998-999
Author(s):  
H.W. Deckman ◽  
B.F. Flannery ◽  
J.H. Dunsmuir ◽  
K.D' Amico
Keyword(s):  
Computed Tomography ◽  
Internal Structure ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Tissue Structure ◽  
Individual Element ◽  
X Ray ◽  
Non Invasive ◽  
Panoramic Imaging ◽  
Three Dimensional Images

We have developed a new X-ray microscope which produces complete three dimensional images of samples. The microscope operates by performing X-ray tomography with unprecedented resolution. Tomography is a non-invasive imaging technique that creates maps of the internal structure of samples from measurement of the attenuation of penetrating radiation. As conventionally practiced in medical Computed Tomography (CT), radiologists produce maps of bone and tissue structure in several planar sections that reveal features with 1mm resolution and 1% contrast. Microtomography extends the capability of CT in several ways. First, the resolution which approaches one micron, is one thousand times higher than that of the medical CT. Second, our approach acquires and analyses the data in a panoramic imaging format that directly produces three-dimensional maps in a series of contiguous stacked planes. Typical maps available today consist of three hundred planar sections each containing 512x512 pixels. Finally, and perhaps of most import scientifically, microtomography using a synchrotron X-ray source, allows us to generate maps of individual element.

Non-invasive investigation of the cross-sectional solids distribution in CFB risers by X-ray computed tomography

2016 ◽  
Vol 297 ◽  
pp. 247-258 ◽  
Author(s):  
Timo Hensler ◽  
Markus Firsching ◽  
Juan Sebastian Gomez Bonilla ◽  
Thorsten Wörlein ◽  
Norman Uhlmann ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cross Sectional ◽  
X Ray ◽  
Non Invasive ◽  
X Ray Computed ◽  
The Cross

scholarly journals Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2021 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

Non-invasive coronary angiography with multi-detector computed tomography: comparison to conventional X-ray angiography

2005 ◽  
Vol 21 (1) ◽  
pp. 63-72 ◽  
Author(s):  
Paul Schoenhagen ◽  
Arthur E. Stillman ◽  
Sandy S. Halliburton ◽  
Stacie A. Kuzmiak ◽  
Tracy Painter ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Coronary Angiography ◽  
Invasive Coronary Angiography ◽  
X Ray ◽  
Non Invasive ◽  
Multi Detector Computed Tomography

scholarly journals Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2020 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

scholarly journals Differential X-ray diagnosis of pseudotuberculous scenario of pulmonary abscess with tuberculous cavities

2021 ◽  
Vol 29 (1) ◽  
pp. 9-20
Author(s):  
R. Yu. Churylin ◽  
I. O. Voronzhev ◽  
Yu. A. Kolomiichenko ◽  
О. О. Коvalova ◽  
V. V. Syrota
Keyword(s):  
Computed Tomography ◽  
Differential Diagnosis ◽  
Thoracic Surgery ◽  
Lung Abscess ◽  
Accurate Diagnosis ◽  
Tuberculosis Patients ◽  
X Ray ◽  
Pulmonary Abscess ◽  
Over Time

Background. Recent decades in Ukraine have been characterized by a significant increase in the number of tuberculosis patients, often with forming cavities of destruction. X-ray diagnosis of lung cavitary lesions is one of the current issues of modern pulmonology and thoracic surgery. Pulmonary abscesses resemble other diseases with destruction and cavities substantiating the need for differential diagnosis with tuberculosis. Purpose – specifying particular scenarios of X-ray presentation of lung abscess and determining the capability of differential diagnosis of pseudotuberculosis with cavities of tuberculosis etiology. Materials and methods. The paper deals with the analysis of X-ray examination of thoracic viscera provided for 252 patients with lung abscess, aged 18 and up to 78. X-ray radiography in two projections, linear and computed tomography (56 patients involved) were performed. All patients underwent a study over time. Results. Almost in most lung abscess cases, there is a need for differential diagnosis with a range of medical entities. The obtained data have made it possible to suggest a classification of X-ray scenarios of lung abscess. The scenarios of X-ray presentation of acute pulmonary abscess are typical and atypical, among those: cystoid, pseudotuberculous, affected 38 patients (15 %), and pulmonary-pleural. The peculiarities of X-ray presentation of pseudotuberculous scenario along with the differences and signs allowing to make an accurate diagnosis have been specified. Conclusions. X-ray study remains an essential in diagnosing purulent-destructive diseases. Being familiar with the scenarios mentioned above and pseudotuberculous one, in particular, will make it possible to significantly improve diagnosis as well as differential diagnosis of pulmonary abscess.

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