A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis

Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of “few data, poor information”, t...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 24; číslo 13; s. 4326
Hlavní autoři: Wang, Yating, Li, Hongjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 03.07.2024
Témata:
adaptive discriminative filtering
Algorithms
Blood vessels
Content analysis
Deep learning
Diabetes
Glaucoma
grey relational analysis
Humans
Image Processing, Computer-Assisted - methods
Medical imaging
Methods
Optics
Retina
retinal vessel segmentation
Retinal Vessels - diagnostic imaging
System theory
Tomography
unsupervised algorithm
grey relational analysis
retinal vessel segmentation
unsupervised algorithm
adaptive discriminative filtering
ISSN:1424-8220, 1424-8220
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Shrnutí:Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of “few data, poor information”, this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24134326