A real-time fuzzy morphological algorithm for retinal vessel segmentation

Bibiloni, Pedro; González-Hidalgo, Manuel; Massanet, Sebastià

doi:10.1007/s11554-018-0748-1

Cited by 17 publications

(21 citation statements)

References 32 publications

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“…Furthermore unsupervised methods are still dominant in works that focus on embedded or mobile systems and on execution speed [6,8,9,24]. These methods often rely on matched filtering, contour tracing and morphological transformation techniques.…”

Section: Related Workmentioning

confidence: 99%

M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Real-World Applications

Laibacher

Weyde

Jalali

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems.The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the parameter count to 0.55M compared to 31.03M in the original U-Net.We have evaluated its performance against a wide body of previously published results on three public datasets. On two of them, the M2U-Net achieves new state-of-the-art performance by a considerable margin. When implemented on a GPU, our method is the first to achieve real-time inference speeds on high-resolution fundus images. We also implemented our proposed network on an ARM-based embedded system where it segments images in between 0.6 and 15 sec, depending on the resolution. Thus, the M2U-Net enables a number of applications of retinal vessel structure extraction, such as early diagnosis of eye diseases, retinal biometric authentication systems, and robot assisted microsurgery.

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Section: Related Workmentioning

confidence: 99%

M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Real-World Applications

Laibacher

Weyde

Jalali

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…The bottom‐hat transform is defined dually as the difference between the closing and the input image, and it is suitable for processing images with bright background and dark object features. Hence, the top‐hat and bottom‐hat operation can be defined as [1, 34, 35]

I_{tophat} = I - ()(, I \circ b)

I_{bottomhat} = ()(, I \cdot b) - I

where I is a greyscale image, b denotes the structural element, “

\circ

” and “

\cdot

” describe the open operation and close operation of the mathematical morphology, respectively. The details of the mathematical morphology operation are shown in [1, 34, 35].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Hence, the top‐hat and bottom‐hat operation can be defined as [1, 34, 35]

I_{tophat} = I - ()(, I \circ b)

I_{bottomhat} = ()(, I \cdot b) - I

where I is a greyscale image, b denotes the structural element, “

\circ

” and “

\cdot

” describe the open operation and close operation of the mathematical morphology, respectively. The details of the mathematical morphology operation are shown in [1, 34, 35]. Combining top‐hat and bottom‐hat transformation, by stretching the contrast between the object and its background, the objects and their details in a greyscale image are highlighted, and then image enhancement processing can be defined as

I_{enhance} = ()(, I_{tophat} + I) - I_{bottomhat}

Mathematical morphological transform operation is set operation, its operation is the interaction between the structure element and the object shape, the shape of the structure element structure determines shape of the extracted object.…”

Section: Proposed Methodsmentioning

confidence: 99%

Retinal vessel segmentation approach based on corrected morphological transformation and fractal dimension

Wang

2019

IET image process

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The morphological structure of retinal vessels plays an important role in analysing and diagnosing fundus disease. In this study, an unsupervised automatic segmentation method for retinal blood vessels with corrected morphological transformation and fractal dimension is proposed. To enhance the contrast between retinal vessels and background in a fundus image, the morphological operator with linear structural elements is used; to remove the lesion and its light reflection, a compensation method is proposed; to extract the objects from a grey image, the multi‐threshold approach is applied; to recognise the blood vessels and noise from the fundus image, a new method based on fractal dimension is presented. The new approach is tested in detail on three public databases STARE, DRIVE and HRF. Experimental results show that the segmentation algorithm is better than other existing unsupervised automatic segmentation algorithms, and the new approach is robust.

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“…Moreover, there is an issue to extract extremely tortuous vessels in case of superimposing large SE. Morphological operations were utilized for both enhancement and RVS [ 2 , 40 – 44 ]. On the other hand, retinal blood vessels of variable thickness at various scales were obtained by multiscale approaches [ 45 – 50 ].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, it allows understanding the colors, dimensions, and shape of objects by processing the amount of light it reflects or emits. Retina image of an eye is captured with a fundus camera [ 2 ]. RGB photographs of the fundus are the protrusion of the internal surface of an eye.…”

Section: Introductionmentioning

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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A real-time fuzzy morphological algorithm for retinal vessel segmentation

Cited by 17 publications

References 32 publications

M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Real-World Applications

M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Real-World Applications

Retinal vessel segmentation approach based on corrected morphological transformation and fractal dimension

A Hybrid Unsupervised Approach for Retinal Vessel Segmentation

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