Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 18, Issue 6, Pages 2586-2597Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2020.2980233
Keywords
Retinal vessels; Image segmentation; Feature extraction; Image edge detection; Transforms; Convolutional neural networks; Convolutional neural network; retinal vessel segmentation; regression; multi-label classification
Categories
Funding
- National Natural Science Foundation of China [61573380, 61702559]
- National Science and Technology Major Project [2018AAA0102102]
- Fundamental Research Funds for the Central Universities of Central South University [2019zzts591]
- Graduate Innovation and Entrepreneurship Program of Central South University [GCX20190883Y]
- 111 project [B18059]
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This paper proposes a local regression scheme and a multi-label classification method to segment small retinal vessels in fundus images. By determining the multi-label based on vessel pattern and training a CNN, the method effectively locates and generates retinal vessel images.
Segmenting small retinal vessels with width less than 2 pixels in fundus images is a challenging task. In this paper, in order to effectively segment the vessels, especially the narrow parts, we propose a local regression scheme to enhance the narrow parts, along with a novel multi-label classification method based on this scheme. We consider five labels for blood vessels and background in particular: the center of big vessels, the edge of big vessels, the center as well as the edge of small vessels, the center of background, and the edge of background. We first determine the multi-label by the local de-regression model according to the vessel pattern from the ground truth images. Then, we train a convolutional neural network (CNN) for multi-label classification. Next, we perform a local regression method to transform the previous multi-label into binary label to better locate small vessels and generate an entire retinal vessel image. Our method is evaluated using two publicly available datasets and compared with several state-of-the-art studies. The experimental results have demonstrated the effectiveness of our method in segmenting retinal vessels.
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