基于混合元启发式改进的reunet ++支持视网膜眼底图像血管分割优化

IF 0.8 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
P. C. Sau, Manish Gupta, A. Bansal
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引用次数: 0

摘要

近年来,一些研究基于无监督和监督算法进行了自动血管分割,以减少用户干扰。深度学习网络已被用于获得高度准确的分割结果。然而,病理信息的错误分割和低微血管分割被认为是现有视网膜血管分割方法中存在的挑战。它还影响不同程度的血管厚度、技术上的上下文特征融合和对细节的感知。本文提出了一种深度学习辅助方法来解决这些挑战。在第一阶段中,使用黑环去除、LAB转换、基于CLAHE的对比度增强和灰度图像所采用的视网膜眼底图像来执行预处理。因此,血管分割是通过一种新的深度学习模型进行的,该模型被称为优化的ResUNet[公式:见正文]。作为对这种深度学习架构的改进,激活函数通过J-AGSO算法进行了优化。基于优化的ResUNet[公式:见正文]的血管分割的目标函数是最小化二进制交叉熵损失函数。此外,图像的后处理是通过中值滤波和二值阈值化来执行的。通过对标准基准数据集的验证,所提出的模型表现出色,性能得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized ResUNet++-Enabled Blood Vessel Segmentation for Retinal Fundus Image Based on Hybrid Meta-Heuristic Improvement
In recent years, several studies have undergone automatic blood vessel segmentation based on unsupervised and supervised algorithms to reduce user interruption. Deep learning networks have been used to get highly accurate segmentation results. However, the incorrect segmentation of pathological information and low micro-vascular segmentation is considered the challenges present in the existing methods for segmenting the retinal blood vessel. It also affects different degrees of vessel thickness, contextual feature fusion in technique, and perception of details. A deep learning-aided method has been presented to address these challenges in this paper. In the first phase, the preprocessing is performed using the retinal fundus images employed by the black ring removal, LAB conversion, CLAHE-based contrast enhancement, and grayscale image. Thus, the blood vessel segmentation is performed by a new deep learning model termed optimized ResUNet[Formula: see text]. As an improvement to this deep learning architecture, the activation function is optimized by the J-AGSO algorithm. The objective function for the optimized ResUNet[Formula: see text]-based blood vessel segmentation is to minimize the binary cross-entropy loss function. Further, the post-processing of the images is carried out by median filtering and binary thresholding. By verifying the standard benchmark datasets, the proposed model outperforms and attains enhanced performance.
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来源期刊
International Journal of Image and Graphics
International Journal of Image and Graphics COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
18.80%
发文量
67
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