Discriminative dictionary learning for retinal vessel segmentation using fusion of multiple features

Yang, Yupei; Shao, Feng; Fu, Zhenqi; Fu, Randi

doi:10.1007/s11760-019-01501-9

Cited by 20 publications

(10 citation statements)

References 24 publications

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“…For this purpose, convolutional neural networks (CNNs) are widely used as the building blocks of deep learning models due to their segmentation performance. Many attempts have been made to prove CNN's ability for retinal vessel segmentation and even they exceeded in segmentation performance as compared to humans and traditional approaches [12–26]. All these techniques utilised deep learning for retinal vessel segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…To focus on thin vessels rather than focusing on the entire structure of the vessels, several recent studies, [16–19], have been carried out. Another recent evidence utilised discriminative dictionary learning (DL) and fusion of multiple features for retinal vessel segmentation [14]. Similarly, a deep learning structure called the Gaussian net (GNET) model combined with a saliency model, was proposed in [13] for retinal vessel segmentation.…”

Section: Introductionmentioning

confidence: 99%

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CPGAN: Conditional patch‐based generative adversarial network for retinal vesselsegmentation

Rammy

Abbas²,

Hassan

et al. 2020

IET image process

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Retinal blood vessels, the diagnostic bio‐marker of ophthalmologic and diabetic retinopathy, utilise thick and thin vessels for diagnostic and monitoring purposes. The existing deep learning methods attempt to segment the retinal vessels using a unified loss function. However, a difference in spatial features of thick and thin vessels and a biased distribution creates an imbalanced thickness, rendering the unified loss function to be useful only for thick vessels. To address this challenge, a patch‐based generative adversarial network‐based technique is proposed which iteratively learns both thick and thin vessels in fundoscopic images. It introduces an additional loss function that allows the generator network to learn thin and thick vessels, while the discriminator network assists in segmenting out both vessels as a combined objective function. Compared with state‐of‐the‐art techniques, the proposed model demonstrates the enhanced accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves on STARE, DRIVE, and CHASEDB1 datasets.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CPGAN: Conditional patch‐based generative adversarial network for retinal vesselsegmentation

Rammy

Abbas²,

Hassan

et al. 2020

IET image process

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show abstract

“…The MCC and CAL values of Chauduri et al [ 31 ], Niemeijer et al [ 11 ], Hoover et al [ 53 ], and B-COSFIRE [ 5 ] are calculated by utilizing their publicly accessible segmented images. The results of Fraz et al [ 68 , 69 ], RUSTICO [ 58 ], Yang et al [ 70 , 71 ], Vega et al [ 72 ], FC-CRF [ 73 ], and UP-CRF [ 73 ] are extracted from their published articles.…”

Section: Experimental Outcomes and Deliberationmentioning

confidence: 99%

A Hybrid Unsupervised Approach for Retinal Vessel Segmentation

Khan

Siddique

Mazzara

2020

BioMed Research International

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Retinal vessel segmentation (RVS) is a significant source of useful information for monitoring, identification, initial medication, and surgical development of ophthalmic disorders. Most common disorders, i.e., stroke, diabetic retinopathy (DR), and cardiac diseases, often change the normal structure of the retinal vascular network. A lot of research has been committed to building an automatic RVS system. But, it is still an open issue. In this article, a framework is recommended for RVS with fast execution and competing outcomes. An initial binary image is obtained by the application of the MISODATA on the preprocessed image. For vessel structure enhancement, B-COSFIRE filters are utilized along with thresholding to obtain another binary image. These two binary images are combined by logical AND-type operation. Then, it is fused with the enhanced image of B-COSFIRE filters followed by thresholding to obtain the vessel location map (VLM). The methodology is verified on four different datasets: DRIVE, STARE, HRF, and CHASE_DB1, which are publicly accessible for benchmarking and validation. The obtained results are compared with the existing competing methods.

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“…In addition to the previous algorithms, in this table, results of Yang et al [17] are also shown (no available data for this algorithm for the individual sets). From the table, it can be seen that in terms of accuracy only the algorithm in Annunziata et al [16] performs better than the proposed algorithm.…”

Section: Sensitivity (Se) = T P/p (1)mentioning

confidence: 99%

“…A discriminative dictionary learning-based retinal vessel segmentation algorithm is proposed, where the proposed algorithm uses fusion of multiple features [17]. Another study [18] utilizes active contours to segment out vessels.…”

Section: Introductionmentioning

confidence: 99%

Retinal vessel segmentation using modified symmetrical local threshold

Özgünalp¹

2020

Turk J Elec Eng & Comp Sci

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Retinal vessel segmentation is important for the identification of many diseases including glaucoma, hypertensive retinopathy, diabetes, and hypertension. Moreover, retinal vessel diameter is associated with cardiovascular mortality. Accurate detection of blood vessels improves the detection of exudates in color fundus images, as well as detection of the retinal nerve, optic disc, or fovea. A retinal vessel is a darker stripe on a lighter background. Thus, the objective is very similar to the lane detection task for intelligent vehicles. A lane on a road is a light stripe on a darker background (i.e. asphalt). For lane detection, the symmetrical local threshold (SLT) is found to be the most robust feature extractor among the tested algorithms in the road marking (ROMA) dataset. Unfortunately, the SLT cannot be applied directly for retinal vessel segmentation. The SLT is a 1D filter and is designed for detecting vertical or close to vertical light stripes with predictable width. In this paper, the SLT is modified to detect dark stripes and four kernels, instead of one, are designed to detect both vertical and horizontal features of a retinal vessel with variable thickness. The proposed algorithm is tested using the High Resolution Fundus (HRF) image database and the accuracy is estimated to be 95.53%. Furthermore, when tested with the Digital Retinal Images for Vessel Segmentation (DRIVE) database, the accuracy is estimated to be 93.69% .

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Discriminative dictionary learning for retinal vessel segmentation using fusion of multiple features

Cited by 20 publications

References 24 publications

CPGAN: Conditional patch‐based generative adversarial network for retinal vesselsegmentation

CPGAN: Conditional patch‐based generative adversarial network for retinal vesselsegmentation

A Hybrid Unsupervised Approach for Retinal Vessel Segmentation

Retinal vessel segmentation using modified symmetrical local threshold

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