Image quality issues for an enhanced vision head up display

1995 ◽  
Vol 10 (3) ◽  
pp. 40-44 ◽  
Author(s):  
J. Todd ◽  
L. Summers ◽  
P. Hammontre
Keyword(s):  
Image Quality ◽  
Quality Issues

MRI of the ankle joint in healthy non-athletes and in marathon runners: image quality issues at 7.0 T compared to 1.5 T

2012 ◽  
Vol 42 (2) ◽  
pp. 261-267 ◽  
Author(s):  
J. M. Theysohn ◽  
O. Kraff ◽  
S. Maderwald ◽  
P. C. Kokulinsky ◽  
M. E. Ladd ◽  
...  
Keyword(s):  
Image Quality ◽  
Ankle Joint ◽  
Marathon Runners ◽  
Quality Issues

Image Quality Issues

EEG - fMRI ◽  
2009 ◽  
pp. 173-199
Author(s):  
David Carmichael
Keyword(s):  
Image Quality ◽  
Quality Issues

Use of radionuclide bone scans in multicenter clinical trials: Opportunities for quality improvement

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e17534-e17534
Author(s):  
A. Lovell ◽  
R. Ford ◽  
T. Monninger ◽  
E. Perlman
Keyword(s):  
Quality Control ◽  
Clinical Trials ◽  
Image Quality ◽  
Hard Copy ◽  
Diagnostic Interpretation ◽  
Quality Issues ◽  
Bone Scans ◽  
Multicenter Clinical Trials ◽  
A Site ◽  
Direct Feedback

e17534 Background: Radionuclide bone scans are widely utilized to screen for osseous metastases given their high sensitivity for detection of metastatic disease. However, the variance in image quality can significantly impact the diagnostic interpretation. Methods: Prospective assessment of image quality on 478 digital and hard copy planar bone scans received as part of several multi-center clinical trials was completed using a two reviewer paradigm. To improve image quality, the following steps were taken: an Internal Image Quality Task Committee was formed, a comprehensive training program for all internal quality control reviewers was completed, and a site qualification process was initiated prior to site selection and/or subject enrollment with direct feedback to the sites. Results: Significant image quality issues potentially limiting the diagnostic interpretation of the radionuclide scans were initially identified in 21% of the submitted radionuclide bone scans. Quality issues were based on: technical factors, poor imaging technique, and subject factors which could not be corrected. A discordance rate of 4.3% among the two reviewers was also measured. After institution of the above steps, the net result was a 58% decrease in the quality control issues that were previously identified. The most significant improvements occurred on the basis of the site qualification process where sites changed from sending hard copy to digital images and there was direct feedback from the Imaging Core Lab to the sites to correct identified issues. Conclusions: Image quality of bone scans in multicenter clinical trials can be improved with direct communication coupled with a site qualification process prior to subject enrollment. We suspect these quality improvements will impact event driven endpoints that rely on the accurate assessment of bone lesions. We further suspect this level of quality control will continue to increase in importance as technology evolves to include hybrid scanning techniques such as SPECT/CT. This parallels our experience in the level of quality control and site qualification required for deployment of FDG-PET imaging in multicenter clinical trials. [Table: see text]

scholarly journals Challenges in Curating 2D Multimedia Data in the Application of Machine Learning in Biodiversity Image Analysis

2021 ◽  
Vol 5 ◽  
Author(s):  
Yasin Bakış ◽  
Xiaojun Wang ◽  
Hank Bart
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Image Quality ◽  
Multimedia Data ◽  
Computational Techniques ◽  
Search Results ◽  
Quality Issues

Over 1 billion biodiversity collection specimens ranging from fungi to fish to fossils are housed in more than 1,600 natural history collections across the United States. The digitization of these specimens has risen significantly within the last few decades and this is only likely to increase, as the use of digitized data gains more importance every day. Numerous experiments with automated image analysis have proven the practicality and usefulness of digitized biodiversity images by computational techniques such as neural networks and image processing. However, most of the computational techniques to analyze images of biodiversity collection specimens require a good curation of this data. One of the challenges in curating multimedia data of biodiversity collection specimens is the quality of the multimedia objects—in our case, two dimensional images. To tackle the image quality problem, multimedia needs to be captured in a specific format and presented with appropriate descriptors. In this study we present an analysis of two image repositories each consisting of 2D images of fish specimens from several institutions—the Integrated Digitized Biocollections (iDigBio) and the Great Lakes Invasives Network (GLIN). Approximately 70 thousand images have been processed from the GLIN repository and 450 thousand images have been processed from the iDigBio repository and their suitability assessed for use in neural network-based species identification and trait extraction applications. Our findings showed that images that came from the GLIN dataset were more successful for image processing and machine learning purposes. Almost 40% of the species have been represented with less than 10 images while only 20% have more than 100 images per species. We identified and captured 20 metadata descriptors that define quality and usability of the image. According to the captured metadata information, 70% of the GLIN dataset images were found to be useful for further analysis according to the overall image quality score. Quality issues with the remaining images included: curved specimens, non-fish objects in the images such as tags, labels and rocks that obstructed the view of the specimen; color, focus and brightness issues; folded or overlapping parts as well as missing parts. We used both the web interface and the API (Application Programming Interface) for downloading images from iDigBio. We searched for all fish genera, families and classes in three different searches with the images-only option selected. Then we combined all of the search results and removed duplicates. Our search on the iDigBio database for fish taxa returned approximately 450 thousand records with images. We narrowed this down to 90 thousand fish images aided by the multimedia metadata with the downloaded search results, excluding some non-fish images, fossil samples, X-ray and CT (computed tomography) scans and several others. Only 44% of these 90 thousand images were found to be suitable for further analysis. In this study, we discovered some of the limitations of biodiversity image datasets and built an infrastructure for assessing the quality of biodiversity images for neural network analysis. Our experience with the fish images gathered from two different image repositories has enabled describing image quality metadata features. With the help of these metadata descriptors, one can simply create a dataset for a desired image quality for the purpose of analysis. Likewise, the availability of the metadata descriptors will help advance our understanding of quality issues, while helping data technicians, curators and the other digitization staff be more aware of multimedia.

Towards Using Live Photos to Mitigate Image Quality Issues In VQA Photography

2021 ◽  
Author(s):  
Lauren Olson ◽  
Chandra Kambhamettu ◽  
Kathleen McCoy
Keyword(s):  
Image Quality ◽  
Quality Issues

Image quality issues in a static image-based telepathology consultation practice

2003 ◽  
Vol 34 (12) ◽  
pp. 1228-1234 ◽  
Author(s):  
Bruce H Williams ◽  
In Soon Hong ◽  
Florabel G Mullick ◽  
Daniel R Butler ◽  
Roderick F Herring ◽  
...  
Keyword(s):  
Image Quality ◽  
Static Image ◽  
Quality Issues
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