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Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter

Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and... PurposeBlood 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.MethodsWe 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.ResultsThe proposed methodology was tested in the DRIVE database, achieving accuracy, sensitivity, and specificity values of 0.9505, 0.7564, and 0.9696, respectively.ConclusionThe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter

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Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2020
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-020-00046-y
Publisher site
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Abstract

PurposeBlood 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.MethodsWe 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.ResultsThe proposed methodology was tested in the DRIVE database, achieving accuracy, sensitivity, and specificity values of 0.9505, 0.7564, and 0.9696, respectively.ConclusionThe 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.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Jun 11, 2020

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