A multi-resolution retinal vessel tracker based on directional smoothing

Englmeier, K.-H.; Bichler, S.; Schmid, K.; Maurino, M; Porta, M.; Bek, T.; Ege, Bernhard Mogens; Larsen, Ole Næsbye; Hejlesen, Ole K.

doi:10.1007/978-3-642-56168-9_207

Cited by 2 publications

(2 citation statements)

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“…Preprocessing has applied in the steps of green channel extraction, mask generation and dilate borders and CLAHE image enhancement [95]. Automatic mask generation was employed to avoid processing of the black border in the images in [96][97]. Thresholding and morphological operations based technique fail to give a perfect mask when the fundus image is not well exposed.…”

Section: Mask Generationmentioning

confidence: 99%

Review of Preprocessing Techniques for Fundus Image Analysis

Joshi¹,

Karule²

2017

AMA_B

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The principal target of preprocessing is to get more appropriate resultant image than its original for further additional analysis. Enhancement of retinal images creates several challenges. The main obstacle is to develop a technique to accommodate the wide variation in contrast inside the image. Necessity of preprocessing methods are for image normalization and to increase the contrast for achieving accurate analysis. This work examined literature in the prior process of digital imaging, in the field of the analysis of fundus image to extract normal and pathologic retinal traits within the context of diabetic retinopathy (DR).

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Section: Mask Generationmentioning

confidence: 99%

Review of Preprocessing Techniques for Fundus Image Analysis

Joshi¹,

Karule²

2017

AMA_B

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“…Since fundoscopic images are the main sources for diagnosis of diabetic retinopathy, manually analyzing those images can be time-consuming and unreliable since there is considerable inconsistency and variability in detecting abnormalities with time. Therefore, biomedical engineers and scientists have pursued computer-aided diagnosis approaches [10] to automate the process. This involves extracting information about the blood vessels and any abnormal patterns from the rest of the fundoscopic image and analyzing them to make more robust clinical decisions for the benefit of the patient.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Diabetic Retinopathy Utilizing Convolutional Neural Networks

RAJARAJESWARI¹,

MOORTHY²,

Bég

2022

J. Mech. Med. Biol.

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Currently, diabetic retinopathy is still screened as a three-stage classification, which is a tedious strategy and along these lines of this paper focuses on developing an improved methodology. In this methodology, we taught a convolutional neural network form on a major dataset, which includes around 45 depictions to do mathematical analysis and characterization. In this paper, DR is constructed, which takes the enter parameters as the HRF fundus photo of the eye. Three classes of patients are considered — healthy patients, diabetic’s retinopathy patients and glaucoma patients. An informed convolutional neural system without a fully connected model will also separate the highlights of the fundus pixel with the help of the enactment abilities like ReLu and softmax and arrangement. The yield obtained from the convolutional neural network (CNN) model and patient data achieves an institutionalized 97% accuracy. Therefore, the resulting methodology is having a great potential benefiting ophthalmic specialists in clinical medicine in terms of diagnosing earlier the symptoms of DR and mitigating its effects.

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A multi-resolution retinal vessel tracker based on directional smoothing

Cited by 2 publications

References 0 publications

Review of Preprocessing Techniques for Fundus Image Analysis

Review of Preprocessing Techniques for Fundus Image Analysis

Simulation of Diabetic Retinopathy Utilizing Convolutional Neural Networks

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