Toward an automatic subjective image quality assessment system

2009 ◽  
Author(s):  
M. Chambah ◽  
S. Ouni ◽  
M. Herbin ◽  
E. Zagrouba
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment System ◽  
Subjective Image Quality

scholarly journals Can visual analogue scale be used in radiologic subjective image quality assessment?

2018 ◽  
Vol 48 (11) ◽  
pp. 1567-1575
Author(s):  
Kathrine Rydén Suther ◽  
Einar Hopp ◽  
Bjarne Smevik ◽  
Arnt Eltvedt Fiane ◽  
Harald Lauritz Lindberg ◽  
...  
Keyword(s):  
Image Quality ◽  
Visual Analogue Scale ◽  
Quality Assessment ◽  
Analogue Scale ◽  
Image Quality Assessment ◽  
Subjective Image Quality

Subjective Image Quality Assessment for Large Samples

2019 ◽  
Vol 56 (13) ◽  
pp. 131103
Author(s):  
刘阳 Yang Liu ◽  
姜润强 Runqiang Jiang ◽  
于洪君 Hongjun Yu ◽  
陈健 Jian Chen
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Subjective Image Quality ◽  
Large Samples

A CNN-BASED RETINAL IMAGE QUALITY ASSESSMENT SYSTEM FOR TELEOPHTHALMOLOGY

2019 ◽  
Vol 19 (05) ◽  
pp. 1950030 ◽  
Author(s):  
XUEWEI WANG ◽  
SHULIN ZHANG ◽  
XIAO LIANG ◽  
CHUN ZHENG ◽  
JINJIN ZHENG ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Retinal Image ◽  
Network Architecture ◽  
Image Quality Assessment ◽  
Binary Classification ◽  
Developed Countries ◽  
Assessment System ◽  
Retinal Image Quality Assessment ◽  
Multi Classification

Oculopathy is a widespread disease among people of all ages around the world. Teleophthalmology can facilitate the ophthalmological diagnosis for less developed countries that lack medical resources. In teleophthalmology, the assessment of retinal image quality is of great importance. In this paper, we propose a no-reference retinal image assessment system based on DenseNet, a convolutional neural network architecture. This system classified fundus images into good and bad quality or five categories: adequate, just noticeable blur, inappropriate illumination, incomplete optic disc, and opacity. The proposed system was evaluated on different datasets and compared to the applications based on other two networks: VGG-16 and GoogLenet. For binary classification, the good-and-bad binary classifier achieves an AUC of 1.000, and the degradation-specified classifiers that distinguish one specified degradation versus the rest achieve AUC values of 0.972, 0.990, 0.982, 0.982 for four categories, respectively. The multi-classification based on DenseNet achieves an overall accuracy of 0.927, which is significantly higher than 0.549 and 0.757 obtained using VGG-16 and GoogLeNet, respectively. The experimental results indicate that the proposed approach produces outstanding performance in retinal image quality assessment and is worth applying in ophthalmological telemedicine applications. In addition, the proposed approach is robust to the image noise. This study fills the gap of multi-classification in retinal image quality assessment.

SU-F-207-02: Use of Postmortem Subjects for Subjective Image Quality Assessment in Abdominal CT Protocols with Iterative Reconstruction

2015 ◽  
Vol 42 (6Part26) ◽  
pp. 3542-3542
Author(s):  
A Mench ◽  
I Lipnharski ◽  
C Carranza ◽  
L Sinclair ◽  
R Lamoureux ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Iterative Reconstruction ◽  
Image Quality Assessment ◽  
Abdominal Ct ◽  
Subjective Image Quality
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