Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks

Song, Joonyoung; Lee, Boreom

doi:10.1109/embc.2017.8036916

Cited by 21 publications

(7 citation statements)

References 17 publications

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“…Our study used digitized photographs to evaluate RVGCs using the SIVA software. Whether assessment using digital photographs and other software (e.g., VAMPIRE software, Universities of Edinburgh and Dundee, UK) 46 or whether new computer algorithms using artificial intelligence and convoluted network to segment and map the retinal vascular tree 47 may provide more precise estimates is unknown.…”

Section: Discussionmentioning

confidence: 99%

The Relationship of Retinal Vessel Geometric Characteristics to the Incidence and Progression of Diabetic Retinopathy

et al. 2018

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Section: Discussionmentioning

confidence: 99%

The Relationship of Retinal Vessel Geometric Characteristics to the Incidence and Progression of Diabetic Retinopathy

et al. 2018

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“…Song and Lee [50] proposed a model using a CNN with pixel-wise path based implementation. This method has consisted of different convolutional and up-sampling layers.…”

Section: ) Neural Network-based Methodsmentioning

confidence: 99%

Comparative Analysis of Vessel Segmentation Techniques in Retinal Images

Imran

Pei

et al. 2019

IEEE Access

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The blood vessels are the primary anatomical structure that can be visible in retinal images. The segmentation of retinal blood vessels has been accepted worldwide for the diagnosis of both cardiovascular (CVD) and retinal diseases. Thus, it requires an appropriate vessel segmentation method for automatic detection of retinal diseases such as diabetic retinopathy and cataract. The detection of retinal diseases using computer-aided diagnosis (CAD) can help people to avoid the risks of visual impairment and save medical resources. This survey presents a comparative analysis of various machine learning and deep learning-based methods for automated blood vessel segmentation in retinal images. This paper briefly describes fundus photography, publicly available retinal databases, pre-processing and post-processing techniques for retinal vessels segmentation. A comprehensive review of the state of the art supervised and unsupervised blood vessel segmentation methodologies are presented in this paper. The objective of this study is to establish a professional structure to familiarize an individual with up-to-date vessel segmentation techniques. Moreover, we compared these approaches to the dataset, evaluation metrics, pre-processing and post-processing steps, feature extraction, segmentation methods, and induced results.

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“…It has higher precision and computational efficiency for regional salient feature recognition. Different from the above visual saliency model, Yang et al [16], Song and Boreom [17], based on the pure mathematics calculation method, the frequency domain residual method (SR algorithm) is proposed. By analyzing the input image spectrum and extracting the residual of the frequency domain image, a fast method for constructing the saliency map in the spatial domain is proposed.…”

Section: Related Workmentioning

confidence: 99%

Automatic Segmentation Algorithm of Ultrasound Heart Image Based on Convolutional Neural Network and Image Saliency

2020

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The emergence of 4D heart images makes the data volume of the images multiply. It is more urgent to require an effective and fast segmentation algorithm. Therefore, a heart image can be accurately segmented from a large amount of image data and an area of interest can be extracted The segmentation algorithm is very necessary. Based on the segmentation and recognition of medical images, this paper proposes a neural network and image saliency based on the obvious difference between the heart image and other tissues in the slice, and the high similarity between adjacent slices in the CT image sequence. Fully automatic segmentation algorithm and 3D visual reconstruction is the segmented heart image. Convolutional neural network is a special deep neural network model of artificial intelligence. Its connections between neurons are not fully connected. The weights of connections between certain neurons in the same layer are shared, and the network model is reduced. The complexity reduces the number of weights. The use of visual saliency techniques to achieve cardiac segmentation based on CT images. An image saliency detection algorithm is adopted to introduce the image segmentation algorithm based on the saliency technique. In this paper, considering the PET image as grayscale image with low resolution, an improved Itti model and an improved GrabCut image segmentation algorithm are proposed to solve the problem of the original algorithm in grayscale image. At the same time, the operation steps of the user division area are cancelled, and the automatic processing is realized, and the running time of the algorithm is improved while optimizing the image segmentation effect. The convolutional neural network is constructed to realize the positioning function of the heart in the image. The original cardiac CT image is cropped by the positioning result, and some non-target areas are removed. A stacking noise reduction self-coding network is constructed, and the network is manually segmented. Training, realize the classification and recognition of the pixels belonging to the heart tissue in the CT image of the heart, and finally realize the segmentation of the heart image based on the classification result. The results of the above segmentation algorithm are quantitatively evaluated and analyzed with the artificial segmentation results, and the segmentation results are visually reconstructed by surface rendering and volume rendering. The algorithm has better accuracy, reliability and higher. The segmentation efficiency is more simplified for user operations.

show abstract

Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks

Cited by 21 publications

References 17 publications

The Relationship of Retinal Vessel Geometric Characteristics to the Incidence and Progression of Diabetic Retinopathy

The Relationship of Retinal Vessel Geometric Characteristics to the Incidence and Progression of Diabetic Retinopathy

Comparative Analysis of Vessel Segmentation Techniques in Retinal Images

Automatic Segmentation Algorithm of Ultrasound Heart Image Based on Convolutional Neural Network and Image Saliency

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