Segmentation of Optic Disc by Localized Active Contour Model in Retinal Fundus Image

License plate detection has always been challenging, especially with the increasing number of smart radars on roads and highways. By 2021, over 1 billion smart traffic radars have been provided worldwide for the gendarmerie as well as for road control officers from the National Security. This paper introduces a novel Arabic and Latin license plate detection and recognition method. In the first stage of the proposed method and after gathering images to build a new dataset, we used YOLOv7 to detect and localize the license plate in the image. The dataset has been labeled manually before feeding it to the detection system. Second, we applied some of the machine learning algorithms to enhance the detected license plate. For this, we employed thresholding as well as the kernel algorithms in order to remove the additional vertical lines in the plate. Afterward, we used the Arabic OCR as well as Easy OCR techniques to recognize the Arabic and the Latin letters on the license plate. The proposed detection algorithm achieved a precision of 97% and a recall of 98%, consequently, we achieved an F1 score of 98% in license plate detection. For image segmentation, while using Arabic OCR and Easy OCR to segment and extract characters from the detected license plate, we achieved an accuracy of 99 %. The proposed method gave satisfactory results for both detection and segmentation.

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“…In this case, 1 is going to be the output value in this pixel, otherwise a 0. Dilation [39] is recognized by the symbol ⊕.…”

Section: Fig 7 E-elan Architecturementioning

confidence: 99%

Arabic and Latin license plate detection and recognition based on YOLOv7 and image processing methods

Moussaoui

Akkad

Benslimane

2023

Preprint

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“…The author introduced an overlapped strategy for the identification of local image patches, which was further added to the U-net framework for more segmentation. The S. H. Bhat and P. Kumar introduced the detection of the optic disc and optic cup with the calculation of segmented regions of the optic cup and optic disc [118]. The retinal segmentation technique was introduced by Pekala et al [119], to segment the optical coherence tomography (OCT) of the retinal images.…”

Section: ) Optic Disc and Optic Cup Segmentation Techniquesmentioning

confidence: 99%

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

et al. 2020

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Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a timeconsuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community. INDEX TERMS Artificial intelligence, deep learning, diabetic retinopathy, fundus images, machine learning, ophthalmology.

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“…Thus machine learning (ML) approaches are attractive for fundus image segmentation. Segmentation methodologies are based in three possible approaches [24]: Form matching based on random forests, support vector machines or Kmeans [14]; techniques based on transformations and active contours [3] oe Deep learning-based methods [1,21,22,26].…”

Section: Fig 1 Optic Disc and Cupmentioning

confidence: 99%

Multidataset Incremental Training for Optic Disc Segmentation

Civit-Masot

Billis

Dominguez-Morales

et al. 2020

Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

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When convolutional neural networks are applied to image segmentation results depend greatly on the data sets used to train the networks. Cloud providers support multi GPU and TPU virtual machines making the idea of cloud-based segmentation as service attractive. In this paper we study the problem of building a segmentation service, where images would come from different acquisition instruments, by training a generalized U-Net with images from a single or several datasets. We also study the possibility of training with a single instrument and perform quick retrains when more data is available. As our example we perform segmentation of Optic Disc in fundus images which is useful for glaucoma diagnosis. We use two publicly available data sets (RIM-One V3, DRISHTI) for individual, mixed or incremental training. We show that multidataset or incremental training can produce results that are similar to those published by researchers who use the same dataset for both training and validation.

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Segmentation of Optic Disc by Localized Active Contour Model in Retinal Fundus Image

Cited by 19 publications

References 13 publications

Arabic and Latin license plate detection and recognition based on YOLOv7 and image processing methods

Arabic and Latin license plate detection and recognition based on YOLOv7 and image processing methods

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

Multidataset Incremental Training for Optic Disc Segmentation

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