Analysis of Noise Removal Techniques on Retinal Optical Coherence Tomography Images

Sheeba, T. M.; Raj, S. Albert Antony

doi:10.14569/ijacsa.2022.0130948

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…De-noising techniques that successfully eliminate speckle noise. Many authors suggested bilateral filtering technique and wiener filtering approach [31][32][33][34][35][36] satisfies this condition. Different filtering methods were used to eliminate the speckle-noise in the OCT images.…”

Section: B Pre-processingmentioning

confidence: 99%

Prediction of Pigment Epithelial Detachment in Optical Coherence Tomography Images using Machine Learning

Sheeba,

Raj

2024

IJACSA

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Pigment Epithelial Detachment (PED) is an eye condition that can affect adults over 50 and eventually harm their central vision. The PED region is positioned between the Bruch's membrane (BM) and the RPE (Retinal Pigment Epithelium) layer. Due to PED, the RPE layer is elevated arc shaped. In this paper, a method to extract the best features to detect pigment epithelial detachment (PED) is proposed. This method uses four-stage strategy that drew inspiration from OCT (Optical Coherence Tomography) imaging to detect the PED. In the first stage, to reduce the speckle-noise, in the second stage, segment the Retinal Pigment Epithelium (RPE) layer. In the third stage, a novel method is proposed to extract the best features to detect PED, and in the fourth stage, machine learning classifiers such as K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve ayes (NB), and Artificial Neural Networks (ANN) were used to significantly predict the PED. For experimental results, 150 retinal OCT volumes were used, 75 normal OCT volumes, and 75 pigment epithelial detachment volumes. Among the 150 images, 80% were used for training and 20% were used for testing. Here, there are 30 images for testing and 120 images for training. To generate a confusion matrix based on the matrices are true positive (TP), false positive (FP), true negative (TN), and false negative (FN). Logistic Regression is predicted more accuracy among the ANN, LR, NB, and KNN models. The LR model predicted accuracy 96.67% for PED detection.

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Section: B Pre-processingmentioning

confidence: 99%

Prediction of Pigment Epithelial Detachment in Optical Coherence Tomography Images using Machine Learning

Sheeba,

Raj

2024

IJACSA

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“…where An extracted speckle noise should be added to the simulated B-scan using the mathematical A-scan (MA_line) and least-square-fit A-scan (LS_line); the frames simulated by the real average A-scan (RA_line) already have the speckle noise. To extract the speckle noise, the MATLAB median and Wiener filter can be used [52]. Figure 7 shows a median and Wiener filter applied on the original demineralized OCT frame with kernel windows of 3 × 3 and 5 × 5, respectively.…”

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confidence: 99%

Auto-Segmentation and Quantification of Non-Cavitated Enamel Caries Imaged with Swept-Source Optical Coherence Tomography

Abdelrehim,

Salah,

Conrad

et al. 2023

Diagnostics

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(1) Background: OCT imaging has been used to assess enamel demineralization in dental research, but it is not yet developed enough to qualify as a diagnostic technique in clinics. The current capabilities of most commercial acquisition software allow for visual and qualitative assessments. There is a need for a fast and verified batch-processing algorithm to segment and analyze demineralized enamel. This study suggests a GUI MATLAB algorithm for the processing and quantitative analysis of demineralized enamel. (2) Methods: A group of artificially demineralized human enamels was in vitro scanned under the OCT, and ROI frames were extracted. By using a selected intensity threshold colormap, Inter- (Ie) and Intra- (Ia) prismatic demineralization can be segmented. A set of quantitative measurements for the average demineralized depth, average line profile, and integrated reflectivity can be obtained for an accurate assessment. Real and simulated OCT frames were used for algorithm verification. (3) Results: A strong correlation between the automated and known Excel measurements for the average demineralization depth was found (R2 > 0.97). (4) Conclusions: OCT image segmentation and quantification of the enamel demineralization zones are possible. The algorithm can assess the future development of a real-time assessment of dental diagnostics using an oral probe OCT.

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Efficient Aerial Images Algorithms over Multi-objects labeling and Semantic Segmentation

Naseer,

Jalal

2024

2024 5th International Conference on Advancements in Computational Sciences (ICACS)

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Analysis of Noise Removal Techniques on Retinal Optical Coherence Tomography Images

Cited by 7 publications

References 24 publications

Prediction of Pigment Epithelial Detachment in Optical Coherence Tomography Images using Machine Learning

Prediction of Pigment Epithelial Detachment in Optical Coherence Tomography Images using Machine Learning

Auto-Segmentation and Quantification of Non-Cavitated Enamel Caries Imaged with Swept-Source Optical Coherence Tomography

Efficient Aerial Images Algorithms over Multi-objects labeling and Semantic Segmentation

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