• Title/Summary/Keyword: Retinal Vessel Segmentation

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Automatic Segmentation of Retinal Blood Vessels Based on Improved Multiscale Line Detection

  • Hou, Yanli
    • Journal of Computing Science and Engineering
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    • v.8 no.2
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    • pp.119-128
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    • 2014
  • The appearance of retinal blood vessels is an important diagnostic indicator of serious disease, such as hypertension, diabetes, cardiovascular disease, and stroke. Automatic segmentation of the retinal vasculature is a primary step towards automatic assessment of the retinal blood vessel features. This paper presents an automated method for the enhancement and segmentation of blood vessels in fundus images. To decrease the influence of the optic disk, and emphasize the vessels for each retinal image, a multidirectional morphological top-hat transform with rotating structuring elements is first applied to the background homogenized retinal image. Then, an improved multiscale line detector is presented to produce a vessel response image, and yield the retinal blood vessel tree for each retinal image. Since different line detectors at varying scales have different line responses in the multiscale detector, the line detectors with longer length produce more vessel responses than the ones with shorter length; the improved multiscale detector combines all the responses at different scales by setting different weights for each scale. The methodology is evaluated on two publicly available databases, DRIVE and STARE. Experimental results demonstrate an excellent performance that approximates the average accuracy of a human observer. Moreover, the method is simple, fast, and robust to noise, so it is suitable for being integrated into a computer-assisted diagnostic system for ophthalmic disorders.

SKU-Net: Improved U-Net using Selective Kernel Convolution for Retinal Vessel Segmentation

  • Hwang, Dong-Hwan;Moon, Gwi-Seong;Kim, Yoon
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.4
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    • pp.29-37
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    • 2021
  • In this paper, we propose a deep learning-based retinal vessel segmentation model for handling multi-scale information of fundus images. we integrate the selective kernel convolution into U-Net-based convolutional neural network. The proposed model extracts and segment features information with various shapes and sizes of retinal blood vessels, which is important information for diagnosing eye-related diseases from fundus images. The proposed model consists of standard convolutions and selective kernel convolutions. While the standard convolutional layer extracts information through the same size kernel size, The selective kernel convolution extracts information from branches with various kernel sizes and combines them by adaptively adjusting them through split-attention. To evaluate the performance of the proposed model, we used the DRIVE and CHASE DB1 datasets and the proposed model showed F1 score of 82.91% and 81.71% on both datasets respectively, confirming that the proposed model is effective in segmenting retinal blood vessels.

Retinal Blood Vessel Segmentation using Deep Learning (딥러닝 기법을 이용한 망막 혈관 분할)

  • Kim, Beomsang;Lee, Ik Hyun
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.5
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    • pp.77-82
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    • 2019
  • Diabetic retinopathy is a complicated form of diabetes due to circulatory disorder in the peripheral blood vessels of the retina. We segment the microvessel for diagnosing diabetic retinophathy. The conventional methods using filter and features can segment the thick blood vessels, but it has relatively weak for segmenting fine blood vessels. In pre-processing step, noise reduction filter and histogram equalization are applied to suppress the noise and enhance the image contrast. Then, deep learning technique is used for pixel-by-pixel segmentation. The accuracy of conventional methods is between 90% to 94%, while the proposed method has improved as 95% accuracy. There is a problem of segmentation error around the optic disc and exudate due to the network depth. However the accuracy can be improved by modifying the network architecture in the future.

Survey of Image Segmentation Algorithms for Extracting Retinal Blood Vessels (망막혈관 검출을 위한 영상분할기법)

  • Kim, Jeong-Hwan;Seo, Seung-Yeon;Song, Chul-Gyu;Kim, Kyeong-Seop
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2019.01a
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    • pp.397-398
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    • 2019
  • 망막혈관 영상에서(retinal image) 혈관의 모양 또는 생성변화를 효과적으로 검진하기 위해서 망막혈관을 자동적으로 분리하는 영상분할 기법의 개발은 매우 중요한 사안이다. 이를 위해서 주로 망막혈관영상의 잡음을 억제하고 또한 혈관의 명암대비도(contrast)를 증가시키는 전처리 과정을 거쳐서 혈관의 국부적인 화소값의 변화, 방향성을 판별하여 혈관을 자동적으로 검출하는 방법들이 제시되어왔으며 최근에는 합성곱 신경망(CNN) 딥러닝 학습모델을 활용한 망막혈관 분리 알고리즘들이 제시되고 있다.

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Extraction and Shape Description of Feature Region on Ocular Fundus Fluorescein Angiogram (형광 안저화상에 관한 특수 영역의 유출 및 모양)

  • Go, Chang-Rim;Ha, Yeong-Ho;Kim, Su-Jung
    • Journal of Biomedical Engineering Research
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    • v.8 no.1
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    • pp.81-86
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    • 1987
  • An image feature extraction method for the low contrast fluoresceln angiogram in dlabetes was studied. To obtain effective image segmentation, an adaptive local difference image is generated and relaxation process are applied to this difference Image. By the use of distance transformed data with segmented image, shape and location of feature regions were obtained. It was shown that the location and shape descriptions of Impaired blood vessel networks and retinal regions are can he utilized for the diagnosis of diabetes and other disease.

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