A Survey on Computer Algorithms for Retinal image Preprocessing and Vessel Segmentation

Maheswari, M.; Murugeswari, G.

doi:10.1109/icict48043.2020.9112470

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…This growing-base strategy is a simple method in the category of area-based methods and is based on the testing the intensity of an initial pixel with all touching pixels and adds them to the first pixel to search again for finding other pixels that belong to the segment. In the image segmentation context, the histogram-based methods are only focused upon the distribution of image pixels in the gray level, whereas the local growth strategies consider that the neighboring pixels also possess adjacent gray levels [ 44 , 46 ].…”

Section: Extended Growth Region Methodsmentioning

confidence: 99%

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

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Section: Extended Growth Region Methodsmentioning

confidence: 99%

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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“…such that the model can learn better [8]. Image processing [21] techniques are employed to reduce noise, resize images, contrast enhancement and image sharpening. The next subsections discuss the specific pre-processing steps that have been deployed in the current work.…”

Section: Preprocessingmentioning

confidence: 99%

Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

Sanjeewani,

Yadav,

Akbar

et al. 2024

Multimed Tools Appl

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Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.

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Section: Preprocessingmentioning

confidence: 99%

Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

NA¹,

Yadav²,

Akbar³

et al. 2021

Preprint

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<div>Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.</div>

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A Survey on Computer Algorithms for Retinal image Preprocessing and Vessel Segmentation

Cited by 7 publications

References 42 publications

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

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