Current Trends and Advances in Image Quality Assessment

Okarma, Krzysztof

doi:10.5755/j01.eie.25.3.23681

Cited by 18 publications

(7 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image quality evaluation methods can be broadly divided into two groups: 1) evaluation using a reference image and 2) without using a reference, though reduced-reference methods are available in some applications 12) .…”

Section: Related Workmentioning

confidence: 99%

[Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Shakhawat

Nakamura

Kimura

et al. 2020

MTA

View full text Add to dashboard Cite

Previously, a referenceless quality evaluation method (RQM) was proposed by Hashimoto et al. to evaluate the quality of the scanned image 7) for the purpose of Abstract A whole slide imaging (WSI) scanner scans pathological-specimens to produce digital images for monitor-based diagnosis and analysis. However, the image quality is sometimes insufficient due to focus-error or noise, in which case the slide needs to be rescanned. In previous work, a referenceless quality evaluation technique was proposed, but some artifacts (i.e. tissue-fold, air-bubble) were detected as false positives. Those artifacts need to be ignored in determining whether rescanning is necessary or not, because they are not caused in the scanning but slide preparation stage. This paper proposes a method for a more practical system to assess WSI quality by distinguishing the origins of quality degradation; the focus-error or noise caused by the scanner and the artifact occurred in the slide preparation. In the method, a support vector machine detects artifacts first, and then quality is evaluated excluding artifact regions. The effectiveness of the proposed system has been experimentally demonstrated.

show abstract

Section: Related Workmentioning

confidence: 99%

[Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Shakhawat

Nakamura

Kimura

et al. 2020

MTA

View full text Add to dashboard Cite

show abstract

“…The learning criterion is MSE, but the use of image quality metrics is better to judge final image conversion. There are many image quality metrics, both objective and perceptual (subjective) [40][41][42].…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

Virtual UV Fluorescence Microscopy from Hematoxylin and Eosin Staining of Liver Images Using Deep Learning Convolutional Neural Network

2020

View full text Add to dashboard Cite

The use of UV (ultraviolet fluorescence) light in microscopy allows improving the quality of images and observation of structures that are not visible in visible spectrum. The disadvantage of this method is the degradation of microstructures in the slide due to exposure to UV light. The article examines the possibility of using a convolutional neural network to perform this type of conversion without damaging the slides. Using eosin hematoxylin stained slides, a database of image pairs was created for visible light (halogen lamp) and UV light. This database was used to train a multi–layer unidirectional convolutional neural network. The results of the study were subjectively and objectively assessed using the SSIM (Structural Similarity Index Measure) and SSIM (structure only) image quality measures. The results show that it is possible to perform this type of conversion (the studies used liver slides for 100× magnification), and in some cases there was an additional improvement in image quality.

show abstract

“…With the rapid growth in computer performance, this area has been gaining popularity. It is essential to evaluate the quality of the image [22] as it affects the reliability of the metrological measurements. In summary of the literature review, several key features can be distinguished among the methods described:…”

Section: Methodsmentioning

confidence: 99%

Research of the Operator’s Advisory System Based on Fuzzy Logic for Pelletizing Equipment

et al. 2019

View full text Add to dashboard Cite

Fertilizer manufacturing in the chemical industry is closely related with agricultural production. More than a half of raw materials for food products are grown by fertilizing plants. The demand of fertilizers has been constantly increasing along the growth of human population. Fertilizer manufacturers face millions of losses each year due to poor quality products. One of the most common reasons is wrong decisions in control of manufacturing processes. Operator’s experience has the highest influence on this. This paper analyzes the pellet measurement data, collected at the fertilizer plant by using indirect measurements. The results of these measurements are used to construct the model of equipment status control, based on the fuzzy logic. The proposed solution allows to respond to changes in production parameters in a 7–10 times faster manner. On average, a manufacturer with production volumes of up to 80 tonnes/hour could have lost about 8400 tonnes/year of high-quality production. The publication seeks symmetry between human and system decision making.

show abstract

Current Trends and Advances in Image Quality Assessment

Cited by 18 publications

References 76 publications

[Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

[Paper] Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Virtual UV Fluorescence Microscopy from Hematoxylin and Eosin Staining of Liver Images Using Deep Learning Convolutional Neural Network

Research of the Operator’s Advisory System Based on Fuzzy Logic for Pelletizing Equipment

Contact Info

Product

Resources

About