Robustness of Image Quality Factors for Environment Illumination

2012 ◽  
Vol E95.A (12) ◽  
pp. 2498-2501 ◽  
Author(s):  
Shogo MORI ◽  
Gosuke OHASHI ◽  
Yoshifumi SHIMODAIRA
Keyword(s):  
Image Quality ◽  
Quality Factors

scholarly journals Correlation of objective image quality and working length measurements in different CBCT machines: An ex vivo study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
T. G. Wolf ◽  
F. Fischer ◽  
R. K. W. Schulze
Keyword(s):  
Image Quality ◽  
Ex Vivo ◽  
Quality Factors ◽  
Study Objective ◽  
Intra Class Correlation ◽  
Working Length ◽  
Length Determination ◽  
Image Quality Measures ◽  
Operator Reproducibility ◽  
Ex Vivo Study

Abstract To investigate potential correlations between objective CBCT image parameters and accuracy in endodontic working length determination ex vivo. Contrast-to-noise ratio (CNR) and spatial resolution (SR) as fundamental objective image parameters were examined using specific phantoms in seven different CBCT machines. Seven experienced observers were instructed and calibrated. The order of the CBCTs was randomized for each observer and observation. To assess intra-operator reproducibility, the procedure was repeated within six weeks with a randomized order of CBCT images. Multivariate analysis (MANOVA) did not reveal any influence of the combined image quality factors CNR and SR on measurement accuracy. Inter-operator reproducibility as assessed between the two observations was poor, with a mean intra-class correlation (ICC) of 0.48 (95%-CI  0.38, 0.59) for observation No. 1. and 0.40 (95%-CI 0.30, 0.51) for observation No. 2. Intra-operator reproducibility pooled over all observers between both observations was only moderate, with a mean ICC of 0.58 (95%-CI 0.52 to 0.64). Within the limitations of the study, objective image quality measures and exposure parameters seem not to have a significant influence on accuracy in determining endodontic root canal lengths in CBCT scans. The main factor of variance is the observer.

Assessment of Some Enhancement Methods of Renal X-ray Image

2020 ◽  
Vol 18 (12) ◽  
pp. 01-05
Author(s):  
Salim J. Attia
Keyword(s):  
Image Quality ◽  
Radiation Dosage ◽  
Histogram Equalization ◽  
Quality Factors ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Quality Image ◽  
Enhancement Method ◽  
Spatial Quality

The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Quality Evaluator (PIQE) and Blind References Image Spatial Quality Evaluator (BRISQE)). The quality of images had been heightened by these methods to support the goals of diagnosis. The results of the chosen enhancement methods of collecting images reflected more qualified images than the original images. According to the results of the quality factors and the assessment of radiology experts, the CLAHE method was the best enhancement method.

41.3: Image Quality Factors for Designs of an Autostereoscopic Display

2014 ◽  
Vol 45 (1) ◽  
pp. 588-590 ◽  
Author(s):  
Yun-Ting Cheng ◽  
Wan-Hsuan Hsu ◽  
Kuo-Chung Huang ◽  
Hoang Yan Lin
Keyword(s):  
Image Quality ◽  
Quality Factors ◽  
Autostereoscopic Display

scholarly journals Entropy Based Data Expansion Method for Blind Image Quality Assessment

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 60 ◽  
Author(s):  
Xiaodi Guan ◽  
Lijun He ◽  
Mengyue Li ◽  
Fan Li
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Large Scale ◽  
Image Quality Assessment ◽  
Expansion Method ◽  
Quality Factors ◽  
Capture Process ◽  
Highly Sensitive ◽  
Blind Image Quality Assessment ◽  
Data Expansion

Image quality assessment (IQA) is a fundamental technology for image applications that can help correct low-quality images during the capture process. The ability to expand distorted images and create human visual system (HVS)-aware labels for training is the key to performing IQA tasks using deep neural networks (DNNs), and image quality is highly sensitive to changes in entropy. Therefore, a new data expansion method based on entropy and guided by saliency and distortion is proposed in this paper. We introduce saliency into a large-scale expansion strategy for the first time. We regionally add distortion to a set of original images to obtain a distorted image database and label the distorted images using entropy. The careful design of the distorted images and the entropy-based labels fully reflects the influences of both saliency and distortion on quality. The expanded database plays an important role in the application of a DNN for IQA. Experimental results on IQA databases demonstrate the effectiveness of the expansion method, and the network’s prediction effect on the IQA databases is found to be improved compared with its predecessor algorithm. Therefore, we conclude that a data expansion approach that fully reflects HVS-aware quality factors is beneficial for IQA. This study presents a novel method for incorporating saliency into IQA, namely, representing it as regional distortion.

scholarly journals Factors Affecting Image Quality and Lesion Evaluability in Breast Diffusion-Weighted MRI: Observations from the ECOG-ACRIN Cancer Research Group Multisite Trial (A6702)

2020 ◽  
Author(s):  
Jennifer G Whisenant ◽  
Justin Romanoff ◽  
Habib Rahbar ◽  
Averi E Kitsch ◽  
Sara M Harvey ◽  
...  
Keyword(s):  
Image Quality ◽  
Secondary Analysis ◽  
Breast Mri ◽  
B Value ◽  
Quality Factors ◽  
Planar Imaging ◽  
Factors Affecting ◽  
Diffusion Weighted ◽  
Imaging Sequence ◽  
Apparent Diffusion

Abstract Objective The A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance. Methods The A6702 protocol was IRB-approved at 10 institutions; participants provided informed consent. In total, 103 women with 142 MRI-detected breast lesions (BI-RADS assessment category 3, 4, or 5) completed the study. DWI was acquired at 1.5T and 3T using a four b-value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping. Results Thirty percent (42/142) of lesions were non-evaluable on DWI; 23% (32/142) with image quality issues, 7% (10/142) with conspicuity and/or localization issues. Misregistration was the only factor associated with non-evaluability (P = 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p <0.03), though not significant after multiplicity correction. The AUC for differentiating benign and malignant lesions increased after excluding non-evaluable lesions, from 0.61 (95% CI: 0.50–0.71) to 0.75 (95% CI: 0.65–0.84). Conclusion Image quality remains a technical challenge in breast DWI, particularly for smaller lesions. Protocol optimization and advanced acquisition and post-processing techniques would help to improve clinical utility.

scholarly journals Blind Image Quality Assessment Without Human Training Using Latent Quality Factors

2012 ◽  
Vol 19 (2) ◽  
pp. 75-78 ◽  
Author(s):  
Anish Mittal ◽  
Gautam S. Muralidhar ◽  
Joydeep Ghosh ◽  
Alan C. Bovik
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Quality Factors ◽  
Blind Image Quality Assessment ◽  
Blind Image
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