Aggregation of Region-based and Boundary-based Knowledge Biased Segmentation for Osteoporosis Detection from X-Ray, Dual X-Ray and CT Images

2020 ◽  
Vol 16 ◽  
Author(s):  
R. Menaka ◽  
R. Ramesh ◽  
R. Dhanagopal
Keyword(s):  
Bone Tissue ◽  
Medical Image ◽  
Imaging Techniques ◽  
Ct Images ◽  
Imaging Method ◽  
X Ray ◽  
Osteoporosis Diagnosis ◽  
Tissue Removal ◽  
Fuzzy Histogram ◽  
Tissue Production

Background: Osteoporosis is a term used to represent the reduced bone density which is caused by insufficient bone tissue production to balance the old bone tissue removal. Medical Imaging procedures such as X-Ray, Dual X-Ray and Computed Tomography (CT) scans are used widely in osteoporosis diagnosis. There are several existing procedures are in practice to assist osteoporosis diagnosis which can operate using a single imaging method. Objective: The purport of this proposed work is to introduce a framework to assist the diagnosis of osteoporosis based on consenting all these X-Ray, Dual X-Ray and CT scan imaging techniques. The proposed work named as "Aggregation of Region-based and Boundary-based Knowledge biased Segmentation for Osteoporosis Detection from X-Ray, Dual X-Ray and CT images" (ARBKSOD) which is the integration of three functional modules. Methods: Fuzzy Histogram Medical Image Classifier (FHMIC), Log-Gabor Transform based ANN Training for osteoporosis detection (LGTAT) and Knowledge biased Osteoporosis Analyzer (KOA). Results: Together, all these three modules make the proposed method ARBKSOD scored the maximum accuracy of 93.11% , the highest precision value of 93.91% while processing the 6th image batch, the highest sensitivity of 92.93%., The highest specificity 93.79% is observed during the experiment by ARBKSOD while processing the 6th image batch. The best average processing time of 10244 mS is achieved by ARBKSOD while processing the 7th image batch. Conclusion: Together, all these three modules make the proposed method ARBKSOD to produce better result.

Full-field X-ray diffraction microscopy using polymeric compound refractive lenses

2014 ◽  
Vol 47 (6) ◽  
pp. 1882-1888 ◽  
Author(s):  
J. Hilhorst ◽  
F. Marschall ◽  
T. N. Tran Thi ◽  
A. Last ◽  
T. U. Schülli
Keyword(s):  
Imaging Techniques ◽  
Light And Electron Microscopy ◽  
Added Value ◽  
Acquisition Time ◽  
Imaging Method ◽  
X Ray Diffraction ◽  
Polymeric Compound ◽  
Full Field ◽  
X Ray ◽  
Diffraction Imaging

Diffraction imaging is the science of imaging samples under diffraction conditions. Diffraction imaging techniques are well established in visible light and electron microscopy, and have also been widely employed in X-ray science in the form of X-ray topography. Over the past two decades, interest in X-ray diffraction imaging has taken flight and resulted in a wide variety of methods. This article discusses a new full-field imaging method, which uses polymer compound refractive lenses as a microscope objective to capture a diffracted X-ray beam coming from a large illuminated area on a sample. This produces an image of the diffracting parts of the sample on a camera. It is shown that this technique has added value in the field, owing to its high imaging speed, while being competitive in resolution and level of detail of obtained information. Using a model sample, it is shown that lattice tilts and strain in single crystals can be resolved simultaneously down to 10−3° and Δa/a= 10−5, respectively, with submicrometre resolution over an area of 100 × 100 µm and a total image acquisition time of less than 60 s.

Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network

2017 ◽  
Vol 23 (2) ◽  
pp. 271-278
Author(s):  
Shoichiro Takao ◽  
Sayaka Kondo ◽  
Junji Ueno ◽  
Tadashi Kondo
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Ct Images ◽  
X Ray

scholarly journals Assessing Automated Machine Learning Service to Detect COVID-19 from X-Ray and CT Images: A Real-Time Smartphone Application Case Study

2020 ◽  
Author(s):  
Mohammad Razib Mustafiz ◽  
Khaled Mohsin
Keyword(s):  
Machine Learning ◽  
Lung Disease ◽  
Real Time ◽  
Web Application ◽  
Medical Image ◽  
Ct Images ◽  
X Ray ◽  
Medical Image Diagnosis ◽  
The World ◽  
Trained Network

AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.

Studies of metaphase chromosomes in the scanning transmission x-ray microscope: Image fidelity, radiation damage and specimen preparation

1992 ◽  
Vol 50 (2) ◽  
pp. 1038-1039
Author(s):  
Shawn Williams ◽  
Xiaodong Zhang ◽  
Susan Lamm ◽  
Jack Van’t Hof
Keyword(s):  
Visible Light ◽  
Radiation Damage ◽  
Cross Section ◽  
Imaging Techniques ◽  
Dose Range ◽  
Specimen Preparation ◽  
X Rays ◽  
Metaphase Chromosomes ◽  
X Ray ◽  
Scanning Transmission

The Scanning Transmission X-ray Microscope (STXM) is well suited for investigating metaphase chromosome structure. The absorption cross-section of soft x-rays having energies between the carbon and oxygen K edges (284 - 531 eV) is 6 - 9.5 times greater for organic specimens than for water, which permits one to examine unstained, wet biological specimens with resolution superior to that attainable using visible light. The attenuation length of the x-rays is suitable for imaging micron thick specimens without sectioning. This large difference in cross-section yields good specimen contrast, so that fewer soft x-rays than electrons are required to image wet biological specimens at a given resolution. But most imaging techniques delivering better resolution than visible light produce radiation damage. Soft x-rays are known to be very effective in damaging biological specimens. The STXM is constructed to minimize specimen dose, but it is important to measure the actual damage induced as a function of dose in order to determine the dose range within which radiation damage does not compromise image quality.

An x-ray microprobe based upon a close-coupled focal-spot / glass capillary arrangement

1992 ◽  
Vol 50 (2) ◽  
pp. 1758-1759
Author(s):  
D. A. Carpenter ◽  
M. A. Taylor
Keyword(s):  
Imaging Techniques ◽  
Fluorescence Analysis ◽  
High Sensitivity ◽  
Specimen Preparation ◽  
X Rays ◽  
Glass Capillary ◽  
Close Coupling ◽  
X Ray ◽  
Point To Point ◽  
Beam Damage

The development of intense sources of x rays has led to renewed interest in the use of microbeams of x rays in x-ray fluorescence analysis. Sparks pointed out that the use of x rays as a probe offered the advantages of high sensitivity, low detection limits, low beam damage, and large penetration depths with minimal specimen preparation or perturbation. In addition, the option of air operation provided special advantages for examination of hydrated systems or for nondestructive microanalysis of large specimens.The disadvantages of synchrotron sources prompted the development of laboratory-based instrumentation with various schemes to maximize the beam flux while maintaining small point-to-point resolution. Nichols and Ryon developed a microprobe using a rotating anode source and a modified microdiffractometer. Cross and Wherry showed that by close-coupling the x-ray source, specimen, and detector, good intensities could be obtained for beam sizes between 30 and 100μm. More importantly, both groups combined specimen scanning with modern imaging techniques for rapid element mapping.

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