Application of automatic threshold in dynamic target recognition with low contrast

Hua, Miao; Guo, Xiaoming; Chen, Yu

doi:10.1117/12.2072569

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Our study adds to the growing body of data suggesting that evidence supporting 24-h IOP profiles for identifying eyes at risk [8,[21][22][23][24] is surprisingly weak [25][26][27]. IOPs from left eyes were charted using a red pencil that has a lower contrast compared to the dark blue used for right eyes analyzed in our prior study, which makes it harder to delineate by automated image recognition [18]. Despite this, IOP data from left eyes was extracted equally well by our custom-made software, HIOP-Reader [9].…”

Section: Discussionmentioning

confidence: 95%

“…Based on billing patterns of eye clinics in German-speaking countries [18,19], we estimate that nycthemeral IOP curves must have been obtained about one million times over the last 100 years [8,[20][21][22]. Our study adds to the growing body of data suggesting that evidence supporting 24-h IOP profiles for identifying eyes at risk [8,[21][22][23][24] is surprisingly weak [25][26][27].…”

Section: Discussionmentioning

confidence: 96%

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Inter-eye correlation analysis of 24-h IOPs and glaucoma progression

Dakroub

Verma-Fuehring

Agorastou

et al. 2022

Graefes Arch Clin Exp Ophthalmol

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Purpose To determine whether 24-h IOP monitoring can be a predictor for glaucoma progression and to analyze the inter-eye relationship of IOP, perfusion, and progression parameters. Methods We extracted data from manually drawn IOP curves with HIOP-Reader, a software suite we developed. The relationship between measured IOPs and mean ocular perfusion pressures (MOPP) to retinal nerve fiber layer (RNFL) thickness was analyzed. We determined the ROC curves for peak IOP (Tmax), average IOP(Tavg), IOP variation (IOPvar), and historical IOP cut-off levels to detect glaucoma progression (rate of RNFL loss). Bivariate analysis was also conducted to check for various inter-eye relationships. Results Two hundred seventeen eyes were included. The average IOP was 14.8 ± 3.5 mmHg, with a 24-h variation of 5.2 ± 2.9 mmHg. A total of 52% of eyes with RNFL progression data showed disease progression. There was no significant difference in Tmax, Tavg, and IOPvar between progressors and non-progressors (all p > 0.05). Except for Tavg and the temporal RNFL, there was no correlation between disease progression in any quadrant and Tmax, Tavg, and IOPvar. Twenty-four-hour and outpatient IOP variables had poor sensitivities and specificities in detecting disease progression. The correlation of inter-eye parameters was moderate; correlation with disease progression was weak. Conclusion In line with our previous study, IOP data obtained during a single visit (outpatient or inpatient monitoring) make for a poor diagnostic tool, no matter the method deployed. Glaucoma progression and perfusion pressure in left and right eyes correlated weakly to moderately with each other.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 96%

Inter-eye correlation analysis of 24-h IOPs and glaucoma progression

Dakroub

Verma-Fuehring

Agorastou

et al. 2022

Graefes Arch Clin Exp Ophthalmol

View full text Add to dashboard Cite

show abstract

“…Here we expanded on this research and analyzed this relationship in left eyes with a focus on inter-eye correlations. IOPs from left eyes were charted using a red pencil that has a lower contrast compared to the dark blue used for right eyes analyzed in our prior study, which makes it harder to delineate by automated image recognition [18]. Despite this, IOP data from left eyes was extracted equally well by our custommade software, HIOP-Reader [9].…”

Section: Discussionmentioning

confidence: 99%

Inter-Eye Correlation Analysis of 24-hour IOPs and Glaucoma Progression

Dakroub¹,

Verma-Fuehring²,

Agorastou³

et al. 2021

Preprint

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Purpose: To determine whether 24-hour IOP monitoring can be a predictor for glaucoma progression and to analyze the inter-eye relationship of IOP, perfusion and progression parameters. Methods: We extracted data from manually drawn IOP curves with HIOP-Reader, a software suite we developed. The relationship between measured IOPs and mean ocular perfusion pressures (MOPP) to retinal nerve fiber layer (RNFL) thickness was analyzed. We determined the ROC curves for peak IOP (Tmax), average IOP (Tavg), IOP variation (IOPvar) and historical IOP cut-off levels to detect glaucoma progression (rate of RNFL loss). Bivariate analysis was conducted to check for various inter-eye relationships. Results: 217 eyes were included. The average IOP was 14.8±3.5 mmHg, with a 24-hour variation of 5.2±2.9 mmHg. 52% of eyes with RNFL data showed disease progression. There was no significant difference in Tmax, Tavg and IOPvar between progressors and non-progressors (all p>0.05). Except for Tavg and the temporal RNFL, there was no correlation between disease progression in any quadrant, Tmax, Tavg and IOPvar. 24-hour and outpatient IOP variables had poor sensitivities and specificities in detecting disease progression. The correlation of inter-eye parameters was moderate; correlation with disease progression was weak. Conclusion: In line with our previous study, IOP data obtained during a single visit (outpatient or inpatient monitoring) make for a poor diagnostic tool, no matter the method deployed. Glaucoma progression and perfusion pressure in left and right eyes correlated weakly to moderately with each other.

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Automatic recognition of erosion area on the slope of tailings dam using region growing segmentation algorithm

Geng

Song

et al. 2022

Arab J Geosci

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Application of automatic threshold in dynamic target recognition with low contrast

Cited by 3 publications

References 2 publications

Inter-eye correlation analysis of 24-h IOPs and glaucoma progression

Inter-eye correlation analysis of 24-h IOPs and glaucoma progression

Inter-Eye Correlation Analysis of 24-hour IOPs and Glaucoma Progression

Automatic recognition of erosion area on the slope of tailings dam using region growing segmentation algorithm

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