Identification and evaluation of osteoporosis diseases using X-Ray scan images

2020 ◽  
Vol 8 (2) ◽  
pp. 101-104
Author(s):  
Vinaya B ◽  
Akila C ◽  
Dinesh Babu J ◽  
Sravan Kumar P
Keyword(s):  
Active Shape Model ◽  
Mineral Density ◽  
Morphometric Measurements ◽  
Shape Model ◽  
X Ray ◽  
Hip Bmd ◽  
Digital Chest ◽  
Chest X Ray ◽  
Inner Diameter ◽  
Model Algorithm

This study aims to inaugurate the comparison among entire hip bone mineral density (RMD) by dual-energy X-ray absorptiometry (DEXA) and a humble clavicle radiogrammetry complete from the ribcage radiograph that provides the result of extraordinary sensitivity and specificity for forecasting full hip BMD from ribcage radiograph for assessment of osteoporosis. Clavicle Radiogrammetry is a method to measure morphometric dimensions like the other and inner diameter of the clavicle at its mid-shaft. Form these measurements; the following Bone Mass Indices are to be calculated, such as the cortical thickness and the Relative thickness. The Clavicle bone is separated from the digital chest X-ray. The Clavicle bone is identified and separated using Active Shape Model algorithm. After this, the morphometric measurements are done. The outcome of the study is whether the digitized chest X-ray analyzed is helpful in the evaluation of osteoporosis or not.

Lung area extraction from 3D chest X-ray CT images using a shape model generated by a variable Bézier surface

2003 ◽  
Vol 34 (4) ◽  
pp. 60-71 ◽  
Author(s):  
Takayuki Kitasaka ◽  
Kensaku Mori ◽  
Jun-ichi Hasegawa ◽  
Jun-ichiro Toriwaki
Keyword(s):  
Ct Images ◽  
Shape Model ◽  
X Ray ◽  
Bézier Surface ◽  
Bezier Surface ◽  
Lung Area ◽  
Chest X Ray

scholarly journals Automated chest radiograph diagnosis: A Twofer for Tuberculosis and Covid-19

2020 ◽  
Author(s):  
Mitushi Verma ◽  
Deepak Patkar ◽  
Madhura Ingalharikar ◽  
Amit Kharat ◽  
Pranav Ajmera ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
X Ray ◽  
Recent Onset ◽  
Radiology Reports ◽  
Fast Spreading ◽  
Digital Chest ◽  
Chest X Ray ◽  
End To End ◽  
The Times

AbstractCoronavirus disease (Covid 19) and Tuberculosis (TB) are two challenges the world is facing. TB is a pandemic which has challenged mankind for ages and Covid 19 is a recent onset fast spreading pandemic. We study these two conditions with focus on Artificial Intelligence (AI) based imaging, the role of digital chest x-ray and utility of end to end platform to improve turnaround times. Using artificial intelligence assisted technology for triage and creation of structured radiology reports using an end to end platform can ensure quick diagnosis. Changing dynamics of TB screening in the times of Covid 19 pandemic have resulted in bottlenecks for TB diagnosis. The paper tries to outline two types of use cases, one is COVID-19 screening in a hospital-based scenario and the other is TB screening project in mobile van setting and discusses the learning of these models which have both used AI for prescreening and generating structured radiology reports.

scholarly journals Lung X-ray Segmentation using Deep Convolutional Neural Networks on Contrast-Enhanced Binarized Images

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 545 ◽  
Author(s):  
Hsin-Jui Chen ◽  
Shanq-Jang Ruan ◽  
Sha-Wo Huang ◽  
Yan-Tsung Peng
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Processing Approach ◽  
Segmentation Accuracy ◽  
Enhancement Method ◽  
Digital Chest ◽  
Chest X Ray ◽  
Model Training

Automatically locating the lung regions effectively and efficiently in digital chest X-ray (CXR) images is important in computer-aided diagnosis. In this paper, we propose an adaptive pre-processing approach for segmenting the lung regions from CXR images using convolutional neural networks-based (CNN-based) architectures. It is comprised of three steps. First, a contrast enhancement method specifically designed for CXR images is adopted. Second, adaptive image binarization is applied to CXR images to separate the image foreground and background. Third, CNN-based architectures are trained on the binarized images for image segmentation. The experimental results show that the proposed pre-processing approach is applicable and effective to various CNN-based architectures and can achieve comparable segmentation accuracy to that of state-of-the-art methods while greatly expediting the model training by up to 20.74 % and reducing storage space for CRX image datasets by down to 94.6 % on average.

Digital Chest X-ray with Computer-aided Detection for TB Screening within Correctional Facilities

2021 ◽  
Author(s):  
Kavindhran Velen ◽  
Farzana Sathar ◽  
Christopher J Hoffmann ◽  
Harry Hausler ◽  
Amanda Fononda ◽  
...  
Keyword(s):  
Correctional Facilities ◽  
Computer Aided Detection ◽  
X Ray ◽  
Computer Aided ◽  
Digital Chest ◽  
Chest X Ray

Symptom and digital chest X-ray TB screening in South African prisons: yield and cost-effectiveness

2020 ◽  
Vol 24 (3) ◽  
pp. 295-302 ◽  
Author(s):  
H-Y. Kim ◽  
V. Zishiri ◽  
L. Page-Shipp ◽  
S. Makgopa ◽  
G. J. Churchyard ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
South African ◽  
Correctional Facilities ◽  
Cost Effectiveness Ratio ◽  
X Ray ◽  
Symptom Screening ◽  
Night Sweats ◽  
Digital Chest ◽  
Chest X Ray ◽  
Optimal Approach

BACKGROUND: Correctional inmates are at a high risk of tuberculosis (TB). The optimal approach to screening this population is unclear.METHODS: We retrospectively reviewed records from TB screening in 64 correctional facilities in South Africa between January 2015 and July 2016. Inmates received symptom screening (any of cough, fever, weight loss, or night sweats) combined with digital chest X-ray (CXR), when available. CXRs were assessed as ‘abnormal' or with no abnormalities. Inmates with either a symptom or an ‘abnormal' CXR were asked to provide a single spot sputum for Xpert® MTB/RIF testing. We estimated the incremental cost-effectiveness ratio (ICER) per additional TB case detected using CXR screening among asymptomatic inmates.RESULTS: Of 61 580 inmates, CXR screening was available for 41 852. Of these, 19 711 (47.1%) had TB symptoms. Among 22 141 inmates without symptoms, 1939/19 783 (9.8%) had an abnormal CXR, and 8 (1.2%) were Xpert-positive among those with Xpert tests done. Of 14 942 who received symptom screening only and had symptoms, 84% (12 616) had an Xpert result, and 105 (0.8%) were positive. The ICER for CXR screening was US$22 278.CONCLUSION: Having CXR in addition to symptom screening increased yield but added considerable cost. A major limitation of screening was the low specificity of the symptom screen.

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