Clinical Ophthalmology: A Systematic Approach, 5th ed.

Lai, Nicky

doi:10.1097/01.opx.0000134900.67908.fc

Cited by 11 publications

(4 citation statements)

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“…The most common indications of DR include dilated retinal veins, hemorrhages, hard exudates and cotton wool spots [ 2 ]. Variation in features of vasculature of retinal images lead to serious diseases such as stroke, diabetes and cardiovascular diseases [ 3 ]. Consequently, an investigation of retinal vessel features can help with recognizing these progressions and permit the patient to make an action while the sickness is still in its initial stage.…”

Section: Introductionmentioning

confidence: 99%

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

2016

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Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts.

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Section: Introductionmentioning

confidence: 99%

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

2016

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“…The major observed signs of DR comprise hemorrhages, dilated retinal veins, cotton wool spots and hard exudates [ 2 ]. Disparities in retinal vascular features are signs of severe ailments, such as diabetes, stroke and cardiovascular diseases [ 3 ]. Retinal vascular changes are irretrievable, even restoration technique would not help the patient to have the same vision capability as was before the disease.…”

Section: Introductionmentioning

confidence: 99%

A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising

et al. 2018

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The exploration of retinal vessel structure is colossally important on account of numerous diseases including stroke, Diabetic Retinopathy (DR) and coronary heart diseases, which can damage the retinal vessel structure. The retinal vascular network is very hard to be extracted due to its spreading and diminishing geometry and contrast variation in an image. The proposed technique consists of unique parallel processes for denoising and extraction of blood vessels in retinal images. In the preprocessing section, an adaptive histogram equalization enhances dissimilarity between the vessels and the background and morphological top-hat filters are employed to eliminate macula and optic disc, etc. To remove local noise, the difference of images is computed from the top-hat filtered image and the high-boost filtered image. Frangi filter is applied at multi scale for the enhancement of vessels possessing diverse widths. Segmentation is performed by using improved Otsu thresholding on the high-boost filtered image and Frangi’s enhanced image, separately. In the postprocessing steps, a Vessel Location Map (VLM) is extracted by using raster to vector transformation. Postprocessing steps are employed in a novel way to reject misclassified vessel pixels. The final segmented image is obtained by using pixel-by-pixel AND operation between VLM and Frangi output image. The method has been rigorously analyzed on the STARE, DRIVE and HRF datasets.

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“…Pre-processing focuses on removing the optic disk structure from the image that helps to improve segmentation efficiency. Segmentation and extraction of blood vessel is the key part in the analysis as their orientations and disparities signs the disease severity and its type like diabetes, cardio vascular problems and others [4]. Classification helps to categorize the image there by enables to predict the occurrence of abnormality.…”

Section: Introductionmentioning

confidence: 99%

An Effective Approach for Blood Vessel Tree Extraction from Retinal Images with Adaptive Weighted Contrast Adjustment Approach

s¹,

K²

2019

IJATCSE

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Retinal vascular analysis is prominent in diagnosing many diseases like retina diabetes, heart stroke, and retinal damages. Vessel extraction from the retinal images is the crucial part in analyzing and detecting the abnormalities in the retina that predict the nature of the disease. In this paper an effective approach of extracting the blood vessel structures from the retinal images is proposed. The approach is integration of pre processing contrast adjustment process, extraction with segmentation and post processing. The method is tested and evaluated with standard databases which conclude that the proposed approach can be directed to classify and localize the abnormalities. The proposed approach is compared against multiple algorithms and observed that the method could attain an improvement in the accuracy of about 10~15%. The results obtained in this work reveal that the proposed approach could able to attain an accuracy of 97%.

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Clinical Ophthalmology: A Systematic Approach, 5th ed.

Cited by 11 publications

References 0 publications

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising

An Effective Approach for Blood Vessel Tree Extraction from Retinal Images with Adaptive Weighted Contrast Adjustment Approach

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