Quantitative Models of Image Quality

1983 ◽  
Vol 27 (1) ◽  
pp. 41-45 ◽  
Author(s):  
Robert J. Beaton
Keyword(s):  
Image Quality ◽  
Digital Images ◽  
Quality Measures ◽  
Quality Metrics ◽  
Statistical Analyses ◽  
Hard Copy ◽  
Average Performance ◽  
Modulation Spectrum ◽  
Quantitative Models ◽  
Soft Copy

Fourteen image quality metrics were evaluated for hard-copy and soft-copy displays of digital images degraded by various levels of noise and blur. All quality metrics were formulated to include the displayed modulation spectrum of the image. The statistical analyses suggested that several of the metrics correlated strongly with performance, and, thus, support the proposed utility of image-dependent quality measures. An MTFA-type metric was shown to correlate highest with the average performance scores across noise and blur conditions.

Managing digital images with SEM viewer™

1994 ◽  
Vol 52 ◽  
pp. 460-461
Author(s):  
Lauraine Denault ◽  
Robin de la Parra ◽  
Claude von Roesgen
Keyword(s):  
Image Quality ◽  
Digital Images ◽  
Significant Loss ◽  
The Other ◽  
Hard Copy ◽  
Network System ◽  
Area Network ◽  
Wide Area Network ◽  
Software Packages ◽  
Significant Barrier

In 1992 Millipore's Corporate SEM Lab made the transition from conventional analog recording of images to digital acquisition and archival. Commonly requested images could be easily and reliably retrieved, without significant loss of image quality or resolution.In theory, digital images could be sent over a network to desktop PC's for review, (rather than producing hard copy of all images). Using established wide area network system, the scope of incorporating digital images significantly broadened. A significant barrier appeared in that the images were acquired using two different hardware/software packages, by two different SEMs (ISI DS130-C and ElectroScan ESEM). Although both generate TIFF images the "flavors" are somewhat different. The TIFF formats were incompatible because the tags used for magnification, micron bar, and descriptor line data were different. As a result, only the image itself generated from one program could be read by the other program, as with all available TIFF readers. Our dilemma was how to enable a single program to recognize and display all necessary information i.e., magnification, descriptor line, and the micron bar with the image.

scholarly journals MRIQC Web-API: Crowdsourcing image quality metrics and expert quality ratings of structural and functional MRI

2017 ◽  
Author(s):  
O. Esteban ◽  
RW. Blair ◽  
DM. Nielson ◽  
JC. Varada ◽  
S. Marrett ◽  
...  
Keyword(s):  
Image Quality ◽  
Cost Effective ◽  
Quality Measures ◽  
Quality Metrics ◽  
Normative Values ◽  
Quality Ratings ◽  
Quality Control Tool ◽  
Image Quality Measures ◽  
Control Tool

SynopsisThe MRIQC Web-API is a resource for scientists to train new automatic quality classifiers. The MRIQC Web-API has collected more than 30K sets of image quality measures automatically extracted from BOLD and T1-weighted scans using MRIQC. MRIQC is an automated MRI Quality Control tool, and here we present an extension to crowdsource these quality metrics along with anonymized metadata and manual quality ratings. This new resource will allow a better understanding of the normative values and distributions of these quality metrics, help determine the relationships between image quality and metadata such as acquisition parameters and finally, provide a cost-effective, easy way to annotate the quality of a large number of cross-site MR scans.

scholarly journals A study of the most important image quality measures

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Rogério Melo Kinape ◽  
Mardson De Freitas Amorim
Keyword(s):  
Image Quality ◽  
Cross Correlation ◽  
Digital Images ◽  
Quality Measures ◽  
Computer Processing ◽  
Quadratic Error ◽  
Signal Noise ◽  
Pixel Correlation ◽  
Image Quality Measures

At the present time there are several statistical computer measures in existence that can be used to quantify the gain or loss of image quality. The challenge consists in guaranteeing that these measures may be reliable for a given goal. This paper presents a study and comparison between several commonly used quality measures (signal/noise relation, average quadratic error, histogram cross-correlation, and pixel correlation) applied to digital images that underwent computer processing to affect their quality.

scholarly journals Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

2020 ◽  
Vol 30 (1) ◽  
pp. 240-257
Author(s):  
Akula Suneetha ◽  
E. Srinivasa Reddy
Keyword(s):  
Image Quality ◽  
Mean Square Error ◽  
Fuzzy Set ◽  
Digital Image ◽  
Gaussian Noise ◽  
Digital Images ◽  
Structural Similarity ◽  
Fuzzy Rules ◽  
Mean Square ◽  
Image Edges

Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

scholarly journals Evaluation of Organ Dose and Image Quality Metrics of Pediatric CT Chest-Abdomen-Pelvis (CAP) Examination: An Anthropomorphic Phantom Study

2021 ◽  
Vol 11 (5) ◽  
pp. 2047
Author(s):  
Nor Azura Muhammad ◽  
Zunaide Kayun ◽  
Hasyma Abu Hassan ◽  
Jeannie Hsiu Ding Wong ◽  
Kwan Hoong Ng ◽  
...  
Keyword(s):  
Image Quality ◽  
Multidetector Ct ◽  
Organ Dose ◽  
Quality Metrics ◽  
Ct Scanner ◽  
Image Quality Metrics ◽  
Tube Current ◽  
Significant Difference ◽  
Noise Ratio ◽  
The Impact

The aim of this study is to investigate the impact of CT acquisition parameter setting on organ dose and its influence on image quality metrics in pediatric phantom during CT examination. The study was performed on 64-slice multidetector CT scanner (MDCT) Siemens Definition AS (Siemens Sector Healthcare, Forchheim, Germany) using various CT CAP protocols (P1–P9). Tube potential for P1, P2, and P3 protocols were fixed at 100 kVp while P4, P5, and P6 were fixed at 80 kVp with used of various reference noise values. P7, P8, and P9 were the modification of P1 with changes on slice collimation, pitch factor, and tube current modulation (TCM), respectively. TLD-100 chips were inserted into the phantom slab number 7, 9, 10, 12, 13, and 14 to represent thyroid, lung, liver, stomach, gonads, and skin, respectively. The image quality metrics, signal to noise ratio (SNR) and contrast to noise ratio (CNR) values were obtained from the CT console. As a result, this study indicates a potential reduction in the absorbed dose up to 20% to 50% along with reducing tube voltage, tube current, and increasing the slice collimation. There is no significant difference (p > 0.05) observed between the protocols and image metrics.

Film-Screen Vs. Digital Radiography in Rheumatoid Arthritis of the Hand

1994 ◽  
Vol 35 (4) ◽  
pp. 311-318 ◽  
Author(s):  
Á. Jónsson ◽  
A. Borg ◽  
P. Hannesson ◽  
K. Herrlin ◽  
K. Jonsson ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Soft Tissue ◽  
Digital Radiography ◽  
Joint Space Narrowing ◽  
Digital Images ◽  
Roc Curves ◽  
Joint Space ◽  
Hard Copy ◽  
Significant Difference ◽  
Soft Tissue Swelling

In a prospective investigation the diagnostic accuracy of film-screen and digital radiography in rheumatoid arthritis of hands was compared. Seventy hands of 36 patients with established rheumatoid arthritis were included in the study. Each of 11 joints in every hand was evaluated regarding the following radiologic parameters: soft tissue swelling, joint space narrowing, erosions and periarticular osteopenia. The digital images were obtained with storage phosphor image plates and evaluated in 2 forms; as digital hard-copy on film and on a monitor of an interactive workstation. The digital images had a resolution of either 3.33 or 5.0 lp/mm. ROC curves were constructed and comparing the area under the curves no significant difference was found between the 3 different imaging forms in either resolution group for soft tissue swelling, joint space narrowing and erosions. The film-screen image evaluation of periarticular osteopenia was significantly better than the digital hard-copy one in the 3.33 lp/mm resolution group, but no significant difference was found in the 5.0 lp/mm group. These results support the view that currently available digital systems are capable of adequate diagnostic performance.

Image-quality metrics for characterizing adaptive optics system performance

1997 ◽  
Vol 36 (26) ◽  
pp. 6583 ◽  
Author(s):  
Robert T. Brigantic ◽  
Michael C. Roggemann ◽  
Kenneth W. Bauer ◽  
Byron M. Welsh
Keyword(s):  
Image Quality ◽  
Adaptive Optics ◽  
System Performance ◽  
Quality Metrics ◽  
Image Quality Metrics ◽  
Adaptive Optics System
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