Retracted: Measurement of Cholesterol Conditions of Eye Image using Fuzzy Local Binary Pattern (FLBP) and Linear Regression

Andana, Shafira Nur; Novamizanti, Ledya; Ramatryana, I Nyoman Apraz

doi:10.1109/icsigsys.2019.8811071

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…In this paper, they use Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Bayesian regularization (BR) classification to diagnose either the subject is suffering from the disease or not. Arcus Senilis [13], [22][23][24][25][26][27] Autonomic Nerve [28] Brain [29] Heart [12], [16], [30][31] Kidney [32] Liver [33] Lung [4], [15], [34] Pancreas [3], [7], [8], [14], [35][36][37] Stomach [38] Iris Recognition System ROI and Accuracy…”

Section: Discussionmentioning

confidence: 99%

Retracted: A Review of Iris Recognition System ROI and Accuracy

Halim

Emanuel

2020

2020 International Conference on Smart Technology and Applications (ICoSTA)

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Section: Discussionmentioning

confidence: 99%

Retracted: A Review of Iris Recognition System ROI and Accuracy

Halim

Emanuel

2020

2020 International Conference on Smart Technology and Applications (ICoSTA)

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“…The NRMSE value for the cholesterol value obtained in reference 42 is 0.2356, and is 0.1522 for reference 43. When the NRMSE value (0.1118) of our proposed method is compared with these values (0.2356 and 0.1522), it can be said that the accuracy of our proposed method is approximately 4% better than 43 and 12% better than. 42 When the multiple linear regression model in Equation ( 10) is applied, Ŷ can be expressed in Equation (19).…”

Section: Resultsmentioning

confidence: 96%

“…There are also studies on the outer part of the iris using sodium ring detection [32][33][34][35][36][37][38][39][40] to determine signs of high cholesterol and that are designed to determine the width of the sodium ring. [41][42][43] In this study, unlike studies in the literature, the relationship between the sodium ring formed around the iris and the cholesterol levels was investigated. Firstly, the characteristics of the sodium ring were first determined using iris pictures captured from people with cholesterol levels (200 mg/dL and above) that are higher than desirable for adults.…”

Section: Introductionmentioning

confidence: 99%

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An alternative approach for determining the cholesterol level: Iris analysis

Özbilgin

Kurnaz

2021

Int J Imaging Syst Tech

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In this study, the relationship between individuals' cholesterol levels and the width of the sodium ring was investigated. Firstly, iris images were taken of 100 individuals with total cholesterol levels of 200 mg/dL and above. The average grey scale value (AGSV) for each iris image with a sodium ring was calculated. Secondly, the relationship between the AGSV and cholesterol levels was investigated. Finally, a new mathematical model was proposed that described the relationship between AGSV and total cholesterol with ~89% accuracy. The relationship between AGSV and high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglyceride levels was calculated with an accuracy of 80.5%, 85.1%, and 79.9%, respectively. In addition, AGSV was determined with 90% accuracy using total cholesterol, HDL, LDL, and triglycerides using multiple linear regression. Although multiple linear regression gives better AGSV, the results have shown that multilayer perceptron (MLP) mapping method gives better accuracy up to 6.62%.

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“…The iris images in this paper are obtained from the patients who had high cholesterol and acquired by one expert operator at the TelkomMedika hospital, Bandung, Indonesia [41], [42]. The test image is 90 colored iris images (RGB channel) with resolution 660 × 603 pixels in 8 bits *.BMP file format.…”

Section: Experiments a Iris Imagesmentioning

confidence: 99%

Basis Pursuit With Sparsity Averaging for Compressive Sampling of Iris Images

et al. 2022

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This paper proposes novel compressed sensing (CS) of colored iris images using three RGB iterations of basis pursuit (BP) with sparsity averaging (SA), called RGB-BPSA. In RGB-BPSA, a sparsity basis is performed using the average of multiple coherent dictionaries to improve the BP reconstruction. In the experiment, first, the decomposition level of wavelet is studied to analyze the best reconstruction result. Second, the effect of compression rate (CR) is considered. Third, the effect of resolution is investigated. Last, the sparse basis of SA is compared to existing basis, i.e., curvelet, Daubechies-1 or haar, and Daubechies-8. The superior RGB-BPSA over existing CS is shown by better visual quality with higher signal to noise ratio (SNR) and structural similarity (SSIM) index in the same CR. In addition, reconstruction time also investigated where RGB-BPSA outperforms curvelet and two times longer than haar and Daubechies-8. INDEX TERMSCompressed sampling, basis pursuit (BP), sparsity averaging, iris images.

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Retracted: Measurement of Cholesterol Conditions of Eye Image using Fuzzy Local Binary Pattern (FLBP) and Linear Regression

Cited by 15 publications

References 5 publications

Retracted: A Review of Iris Recognition System ROI and Accuracy

Retracted: A Review of Iris Recognition System ROI and Accuracy

An alternative approach for determining the cholesterol level: Iris analysis

Basis Pursuit With Sparsity Averaging for Compressive Sampling of Iris Images

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