Abstract
Purpose
Blood vessel segmentation is the most important step for detecting changes in retinal vascular structures in retinal images. While these images are widely used in clinical diagnosis, they are generally degraded by noise and are limited by low contrast. In this paper, we address the problem of improving fundus image quality for blood vessel detection.
Methods
We used contrast limited adaptive histogram equalization (CLAHE) to improve contrast and the Wiener filter for noise reduction. A multilayer artificial neural network was used to optimize the values from CLAHE and the Wiener filter for blood vessel segmentation. Furthermore, several training and classification rounds were performed (3240, with 200 epochs each), using a combination of CLAHE and Wiener parameters and a fixed network configuration.
Results
The proposed methodology was tested in the DRIVE database, achieving accuracy, sensitivity, and specificity values of 0.9505, 0.7564, and 0.9696, respectively.
Conclusion
The results were encouraging for almost all metrics and comparable to those of state-of-the-art blood vessel segmentation processes. Therefore, the parameter set effectively improved the fundus image quality for blood vessel segmentation, relative to the classification. These results are important since the more precise the segmentation step is, the greater the chances are of building a robust and specialized diagnostic system.
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Author Jucelino Cardoso Marciano dos Santos declares that he has no conflict of interest. Author Gilberto Arantes Carrijo declares that he has no conflict of interest. Author Cristiane de Fátima dos Santos Cardoso declares that she has no conflict of interest. Author Júlio César Ferreira declares that he has no conflict of interest. Author Pedro Moises Sousa declares that he has no conflict of interest. Author Ana Cláudia Patrocínio declares that she has no conflict of interest.
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dos Santos, J.C.M., Carrijo, G.A., de Fátima dos Santos Cardoso, C. et al. Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter. Res. Biomed. Eng. 36, 107–119 (2020). https://doi.org/10.1007/s42600-020-00046-y
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DOI: https://doi.org/10.1007/s42600-020-00046-y