The effect of different statistical approaches on image quality data obtained from radiological examinations

Radiography ◽  
2021 ◽  
Author(s):  
J. Saint ◽  
A. England ◽  
A.M. Ali ◽  
L. Bonnett
Keyword(s):  
Image Quality ◽  
Quality Data ◽  
Statistical Approaches ◽  
Radiological Examinations

scholarly journals Face Image Quality Assessment: A Literature Survey

2022 ◽  
Author(s):  
Torsten Schlett ◽  
Christian Rathgeb ◽  
Olaf Henniger ◽  
Javier Galbally ◽  
Julian Fierrez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Face Image ◽  
Quality Data ◽  
Learning Approaches ◽  
The Face ◽  
Future Work

The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.

Comprehensive optimization process of paranasal sinus radiography

2009 ◽  
Vol 50 (3) ◽  
pp. 327-333 ◽  
Author(s):  
S. Saarakkala ◽  
K. Nironen ◽  
H. Hermunen ◽  
J. Aarnio ◽  
J.O. Heikkinen
Keyword(s):  
Image Quality ◽  
Radiation Exposure ◽  
Paranasal Sinus ◽  
Digital Radiography ◽  
Optimization Process ◽  
Clinical Image ◽  
Central Hospital ◽  
Cr System ◽  
Sinus Radiography ◽  
Radiological Examinations

Background: The optimization of radiological examinations is important in order to reduce unnecessary patient radiation exposure. Purpose: To perform a comprehensive optimization process for paranasal sinus radiography at Mikkeli Central Hospital, Finland. Material and Methods: Patients with suspicion of acute sinusitis were imaged with a Kodak computed radiography (CR) system ( n=20) and with a Philips digital radiography (DR) system ( n=30) using focus-detector distances (FDDs) of 110 cm, 150 cm, or 200 cm. Patients’ radiation exposure was determined in terms of entrance surface dose and dose-area product. Furthermore, an anatomical phantom was used for the estimation of point doses inside the head. Clinical image quality was evaluated by an experienced radiologist, and physical image quality was evaluated from the digital radiography phantom. Results: Patient doses were significantly lower and image quality better with the DR system compared to the CR system. The differences in patient dose and physical image quality were small with varying FDD. Clinical image quality of the DR system was lowest with FDD of 200 cm. Further, imaging with FDD of 150 cm was technically easier for the technologist to perform than with FDD of 110 cm. Conclusion: After optimization, it was recommended that the DR system with FDD of 150 cm should always be used at Mikkeli Central Hospital. We recommend this kind of comprehensive approach in all optimization processes of radiological examinations.

scholarly journals Perbaikan Citra Menggunakan Metode Contrast Stretching

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Supiyanto Supiyanto ◽  
◽  
Titik Suparwati ◽  
Keyword(s):  
Image Quality ◽  
Image Data ◽  
Quality Data ◽  
Good Information ◽  
Application Development ◽  
Improve Image Quality ◽  
Sharp Image ◽  
Contrast Stretching ◽  
True Color ◽  
Matlab Programming

Contrasting images that are not good because they are too bright or too dark cannot provide good information. Therefore, a method is needed to improve the image quality, so that the information in the image can be conveyed properly. Contrast stretching is one of the methods for improving image quality. With this method is expected to produce a new image that is better. The purpose of this research is to apply contrast stretching method to an application or software that can be used to improve image quality. Data used in this study in the form of grayscale image data and RGB imagery (true color), with the format . BMP or .JPG, while the application development uses the Matlab programming language.The results of the study, contrast stretching method can be used to repair image that affects bad or poor image quality such as too bright / dark image, less sharp image, blurry, and so on. Contrast stretching method can also be used to improve image enhancement by leveling the histogram that was collected in an area, so that the information contained in the image is more clearly visible compared to the original image.

scholarly journals Water Quality Modelling of River Mahanadi using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR)

2021 ◽  
Vol 10 (1) ◽  
pp. 83-98
Author(s):  
Chandra Sekhar Matli ◽  
Nivedita
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Quality Management ◽  
Surface Water Quality ◽  
Principal Component ◽  
Quality Data ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Sampling Locations ◽  
Statistical Approaches

Surface water quality is one of the critical environmental concerns of the globe and water quality management is top priority worldwide. In India, River water quality has considerably deteriorated over the years and there is an urgent need for improving the surface water quality. The present study aims at use of multivariate statistical approaches for interpretation of water quality data of Mahanadi River in India. Monthly water quality data pertaining to 16 parameters collected from 12 sampling locations on the river by Central Water Commission (CWC) and Central Pollution Control Board (CPCB) is used for the study. Cluster analysis (CA), is used to group the sampling locations on the river into homogeneous clusters with similar behaviour. Principal component analysis (PCA) is quite effective in identifying the critical parameters for describing the water quality of the river in dry and monsoon seasons. PCA and Factor Analysis (FA) was effective in explaining 69 and 66% of the total cumulative variance in the water quality if dry and wet seasons respectively. Industrial and domestic wastewaters, soil erosion and weathering, soil leaching organic pollution and natural pollution were identified as critical sources contribution to pollution of river water. However, the quantitative contributions were variable based on the season. Results of multiple linear regression (MLR) are effective in explaining the factor loadings and source contributions for most water quality parameters. The study results indicate suitability of multivariate statistical approaches to design and plan sampling and sampling programs for controlling water quality management programs in river basins.

scholarly journals Optimization of image quality in pulmonary CT angiography with low dose of contrast material

2017 ◽  
Vol 23 (2) ◽  
pp. 43-46 ◽  
Author(s):  
Abed Al Nasser Assi ◽  
Ali Abu Arra
Keyword(s):  
Pulmonary Embolism ◽  
Image Quality ◽  
Contrast Agent ◽  
Image Noise ◽  
Quality Data ◽  
Pulmonary Vessel ◽  
Significant Difference ◽  
Group A ◽  
Noise Ratio ◽  
Group B

Abstract Aim: The aim of this study was to compare objective image quality data for patient pulmonary embolism between a conventional pulmonary CTA protocol with respect to a novel acquisition protocol performed with optimize radiation dose and less amount of iodinated contrast medium injected to the patients during PE scanning. Materials and Methods: Sixty- four patients with Pulmonary Embolism (PE) possibility, were examined using angio-CT protocol. Patients were randomly assigned to two groups: A (16 women and 16 men, with age ranging from 19-89 years) mean age, 62 years with standard deviation 16; range, 19-89 years) - injected contrast agent: 35-40 ml. B (16 women and 16 men, with age ranging from 28-86 years) - injected contrast agent: 70-80 ml. Other scanning parameters were kept constant. Pulmonary vessel enhancement and image noise were quantified; signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective vessel contrast was assessed by two radiologists in consensus. Result: A total of 14 cases of PE (22 %) were found in the evaluated of subjects (nine in group A, and five in group B). All PE cases were detected by the two readers. There was no significant difference in the size or location of the PEs between the two groups, the average image noise was 14 HU for group A and 19 HU for group B. The difference was not statistically significant (p = 0.09). Overall, the SNR and CNR were slightly higher on group B (24.4 and 22.5 respectively) compared with group A (19.4 and 16.4 respectively), but those differences were not statistically significant (p = 0.71 and p = 0.35, respectively). Conclusion and Discussion: Both groups that had been evaluated by pulmonary CTA protocol allow similar image quality to be achieved as compared with each other’s, with optimize care dose for both protocol and contrast volume were reduced by 50 % in new protocol comparing to the conventional protocol.

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