The Use of Fourier Phase Symmetry for Thin Vessel Detection in Retinal Fundus Images

Khan, Muhammad Mushtaq; Khan, Tariq M.; Aziz, Kahtan; Ahmad, Sayed Sayeed; Mir, Nighat; Elbakush, E.

doi:10.1109/isspit47144.2019.9001804

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…Along these lines, it has long been thought that detecting retinal blood vessels is the most difficult problem, and it is frequently thought that it is the most crucial part of an automated computer-aided diagnostic (CAD) system [1,9]. This is because the vessels in the retina are hard to see because of their tortuous shape, density, diameter and branching pattern [10]. Even more challenging to identify are the centerline reflex and the many components that make up the retina, including the macula, optic cup/disc, exudates and so on, all of which may have lesions or other flaws.…”

Section: Introductionmentioning

confidence: 99%

G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation

et al. 2022

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In recent years, convolutional neural network architectures have become increasingly complex to achieve improved performance on well-known benchmark datasets. In this research, we have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps, hence reducing the complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The suggested architecture is analysed using three publicly available datasets for retinal vessel segmentation, namely DRIVE, CHASE and STARE datasets. The proposed G-Net light achieves an average accuracy of 0.9686, 0.9726, 0.9730 and F1-score of 0.8202, 0.8048, 0.8178 on DRIVE, CHASE, and STARE datasets, respectively. The proposed G-Net light achieves state-of-the-art performance and outperforms other lightweight vessel segmentation architectures with fewer trainable number of parameters.

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Section: Introductionmentioning

confidence: 99%

G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation

et al. 2022

Self Cite

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“…As a result, the number and type of lesions that form on the surface of the retina affect the severity and diagnosis of the disease. As a result, the effectiveness of an automated system for extensive screening is anticipated to depend on the precision of segmenting blood vessels, optical cup/disc, and retinal lesions [4], [5]. Along these lines, it has long been thought that detecting retinal blood vessels is the most difficult problem, and it is frequently thought that it is the most crucial part of an automated computer-aided diagnostic (CAD) system [1], [6].…”

Section: Introductionmentioning

confidence: 99%

G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation

Iqbal¹,

Naqvi²,

Ahmed³

et al. 2022

Preprint

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Convolutional neural network architectures have become increasingly complex, which has improved the performance slowly on well-known benchmark datasets in the recent years. In this research, we have analyzed the true need for such complexity. We have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps and complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The investigations on the proposed architecture are evaluated on three retinal vessel segmentation publicly available datasets. The proposed G-Net light outperforms other vessel segmentation architectures by reducing the number of trainable parameters..

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“…Retinal vessel segmentation has attracted significant attention from engineers and scientists, resulting in a wide range of state of the art methods [13][14][15][16][17][18][19]. However, effective segmentation of retinal vessels is still an open problem due to various challenges which involve sharp variations in vessel size, shape, and orientation, not to mention the low intensity, branching, and vessel crossovers.…”

Section: Introductionmentioning

confidence: 99%

“…Earlier, classical image segmentation strategies were tailored to detect and segment out vessel patterns. These techniques identify vessels based on width, size, shape and orientation of vessels and hence are referred to as unsupervised methods [14][15][16][20][21][22]. However, these methods can only capture limited types of vessels due to sharp variations in their shapes and sizes.…”

Section: Introductionmentioning

confidence: 99%

DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

Raza

Naveed

Akram³

et al. 2021

PLoS ONE

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In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.

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The Use of Fourier Phase Symmetry for Thin Vessel Detection in Retinal Fundus Images

Cited by 10 publications

References 24 publications

G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation

G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation

G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation

DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

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