A Multi-Anatomical Retinal Structure Segmentation System for Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

Almotiri, Jasem; Elleithy, Khaled; Elleithy, Abdelrahman

doi:10.1109/jtehm.2018.2835315

Cited by 24 publications

(7 citation statements)

References 66 publications

(84 reference statements)

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“…The thresholding approach has received wide attention because of its low complexity, low storage requirements and low processing time [16]. However, it is not effective if the different regions in the image under consideration have not well-defined areas (e.g., different objects with similar gray areas), or the pixel intensity histograms are unimodal [17]. For detailed descriptions of recent thresholding segmentation approaches, readers may refer to [18], [17], [19], [20].…”

Section: Related Workmentioning

confidence: 99%

A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread

et al. 2022

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In this paper, we address the problem of automatic image segmentation methods applied to the partial automation of the production process of a traditional Sardinian flatbread called pane Carasau for assuring quality control. The study focuses on one of the most critical activities for obtaining an efficient degree of automation: the estimation of the size and shape of the bread sheets during the production phase, to study the shape variations undergone by the sheet depending on some environmental and production variables. The knowledge can thus be used to create a system capable of predicting the quality of the shape of the dough produced and empower the production process. We implemented an image acquisition system and created an efficient machine learning algorithm, based on support vector machines, for the segmentation and estimation of image measurements for Carasau bread. Experiments demonstrated that the method can successfully achieve accurate segmentation of bread sheets images, ensuring that the dimensions extracted are representative of the sheets coming from the production process. The algorithm proved to be fast and accurate in estimating the size of the bread sheets in various scenarios that occurred over a year of acquisitions. The maximum error committed by the algorithm is equal to the 2.2% of the pixel size in the worst scenario and to 1.2% elsewhere.

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Section: Related Workmentioning

confidence: 99%

A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread

et al. 2022

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“…Using fuzzy morphological techniques, DR diagnosis can be achieved with an accuracy of 93.8% [12]. Similarly, Almotiri et al applied fuzzy c-means (FCM) clustering for image segmentation, and DR detection was achieved with 95.88% accuracy [13].…”

Section: Introductionmentioning

confidence: 99%

Automatic Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Improved Discrete Particle Swarm Optimization

Bhimavarapu

Battineni

2022

JPM

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Diabetic retinopathy (DR) is one of the most important microvascular complications associated with diabetes mellitus. The early signs of DR are microaneurysms, which can lead to complete vision loss. The detection of DR at an early stage can help to avoid non-reversible blindness. To do this, we incorporated fuzzy logic techniques into digital image processing to conduct effective detection. The digital fundus images were segmented using particle swarm optimization to identify microaneurysms. The particle swarm optimization clustering combined the membership functions by grouping the high similarity data into clusters. Model testing was conducted on the publicly available dataset called DIARETDB0, and image segmentation was done by probability-based (PBPSO) clustering algorithms. Different fuzzy models were applied and the outcomes were compared with our probability discrete particle swarm optimization algorithm. The results revealed that the proposed PSO algorithm achieved an accuracy of 99.9% in the early detection of DR.

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“…However, due to the lack of a public database for ROP and hence the difficulty in obtaining a large clinical dataset, the development of DL algorithms for diagnosing ROP lags behind other retinal diseases. Some studies have used DL models to process retinal images by vessel segmentation or zone identification and have recommended the application of the feature-based images for further clinical diagnosis or DL model building [10][11][12]. Some studies followed this flow to build an entire DL model for diagnosing ROP.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Deep Learning Algorithms for the Diagnosis of Retinopathy of Prematurity by Fundus Images: A Systematic Review and Meta-Analysis

Zhang

Liu

Mitsuhashi

et al. 2021

Journal of Ophthalmology

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Background. Retinopathy of prematurity (ROP) occurs in preterm infants and may contribute to blindness. Deep learning (DL) models have been used for ophthalmologic diagnoses. We performed a systematic review and meta-analysis of published evidence to summarize and evaluate the diagnostic accuracy of DL algorithms for ROP by fundus images. Methods. We searched PubMed, EMBASE, Web of Science, and Institute of Electrical and Electronics Engineers Xplore Digital Library on June 13, 2021, for studies using a DL algorithm to distinguish individuals with ROP of different grades, which provided accuracy measurements. The pooled sensitivity and specificity values and the area under the curve (AUC) of summary receiver operating characteristics curves (SROC) summarized overall test performance. The performances in validation and test datasets were assessed together and separately. Subgroup analyses were conducted between the definition and grades of ROP. Threshold and nonthreshold effects were tested to assess biases and evaluate accuracy factors associated with DL models. Results. Nine studies with fifteen classifiers were included in our meta-analysis. A total of 521,586 objects were applied to DL models. For combined validation and test datasets in each study, the pooled sensitivity and specificity were 0.953 (95% confidence intervals (CI): 0.946–0.959) and 0.975 (0.973–0.977), respectively, and the AUC was 0.984 (0.978–0.989). For the validation dataset and test dataset, the AUC was 0.977 (0.968–0.986) and 0.987 (0.982–0.992), respectively. In the subgroup analysis of ROP vs. normal and differentiation of two ROP grades, the AUC was 0.990 (0.944–0.994) and 0.982 (0.964–0.999), respectively. Conclusions. Our study shows that DL models can play an essential role in detecting and grading ROP with high sensitivity, specificity, and repeatability. The application of a DL-based automated system may improve ROP screening and diagnosis in the future.

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A Multi-Anatomical Retinal Structure Segmentation System for Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

Cited by 24 publications

References 66 publications

A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread

A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread

Automatic Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Improved Discrete Particle Swarm Optimization

Accuracy of Deep Learning Algorithms for the Diagnosis of Retinopathy of Prematurity by Fundus Images: A Systematic Review and Meta-Analysis

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