scholarly journals Looking towards objective quality evaluation in colonoscopy: Analysis of visual gaze patterns

2016 ◽  
Vol 31 (3) ◽  
pp. 604-609 ◽  
Author(s):  
Matthew J Edmondson ◽  
Philip H Pucher ◽  
Kumuthan Sriskandarajah ◽  
Jonathan Hoare ◽  
Julian Teare ◽  
...  
2013 ◽  
Vol 32 (3) ◽  
pp. 710-714
Author(s):  
Jin-jin WEI ◽  
Su-mei LI ◽  
Wen-juan LIU ◽  
Yan-jun ZANG

2013 ◽  
Vol 411-414 ◽  
pp. 1362-1367 ◽  
Author(s):  
Qing Lan Wei ◽  
Yuan Zhang

This paper presents the thoughts about application of saliency map to the video objective quality evaluation system. It computes the SMSE and SPSNR values as the objective assessment scores according to the saliency map, and compares with conditional objective evaluation methods as PSNR and MSE. Experimental results demonstrate that this method can well fit the subjective assessment results.


HPB ◽  
2020 ◽  
Author(s):  
Chetanya Sharma ◽  
Harsmirat Singh ◽  
Felipe Orihuela-Espina ◽  
Ara Darzi ◽  
Mikael H. Sodergren

2012 ◽  
Vol 10 (s1) ◽  
pp. s11101-311103
Author(s):  
Shanbo Gu Shanbo Gu ◽  
Feng Shao Feng Shao ◽  
Gangyi Jiang Gangyi Jiang ◽  
Mei Yu Mei Yu

Author(s):  
Roopak R. Tamboli ◽  
Aron Cserkaszky ◽  
Peter A. Kara ◽  
Attila Barsi ◽  
Maria G. Martini

2019 ◽  
Vol 11 (10) ◽  
pp. 204 ◽  
Author(s):  
Dogan ◽  
Haddad ◽  
Ekmekcioglu ◽  
Kondoz

When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance.


2019 ◽  
Vol 2019 (10) ◽  
pp. 312-1-312-7 ◽  
Author(s):  
Stuart Perry ◽  
António Pinheiro ◽  
Emil Dumic ◽  
Luis A da Silva Cruz

2011 ◽  
Vol 106 (6) ◽  
pp. 1070-1074 ◽  
Author(s):  
Cristina Almansa ◽  
Muhammad W Shahid ◽  
Michael G Heckman ◽  
Susan Preissler ◽  
Michael B Wallace

Endoscopy ◽  
2018 ◽  
Vol 50 (07) ◽  
pp. 701-707 ◽  
Author(s):  
Mariam Lami ◽  
Harsimrat Singh ◽  
James Dilley ◽  
Hajra Ashraf ◽  
Matthew Edmondon ◽  
...  

Abstract Background The adenoma detection rate (ADR) is an important quality indicator in colonoscopy. The aim of this study was to evaluate the changes in visual gaze patterns (VGPs) with increasing polyp detection rate (PDR), a surrogate marker of ADR. Methods 18 endoscopists participated in the study. VGPs were measured using eye-tracking technology during the withdrawal phase of colonoscopy. VGPs were characterized using two analyses – screen and anatomy. Eye-tracking parameters were used to characterize performance, which was further substantiated using hidden Markov model (HMM) analysis. Results Subjects with higher PDRs spent more time viewing the outer ring of the 3 × 3 grid for both analyses (screen-based: r = 0.56, P = 0.02; anatomy: r = 0.62, P < 0.01). Fixation distribution to the “bottom U” of the screen in screen-based analysis was positively correlated with PDR (r = 0.62, P = 0.01). HMM demarcated the VGPs into three PDR groups. Conclusion This study defined distinct VGPs that are associated with expert behavior. These data may allow introduction of visual gaze training within structured training programs, and have implications for adoption in higher-level assessment.


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