针对眼底图像中存在大量不规则、噪声干扰严重、边界模糊、分割难度较大的细小血管的问题，提出一种基于多方向特征和连通性检测的眼底图像分割方法MDF_Net&CD(Multi-Directional Features neural Network and Connectivity Detection)。设计了一个以像素点不同方向特征向量为输入的深度神经网络模型MDF_Net(Multi-Directional Features neural Network)，利用MDF_Net对眼底图像进行初步分割;提出连通性检测算法，根据血管的几何特征，对MDF_Net的初步分割结果进一步修订。在公开的眼底图像数据集上，将MDF_Net&CD与近期有代表性的分割方法进行实验对比，结果表明MDF_Net&CD各项评估指标均衡，敏感度、F1值和准确率优于其他方法。该方法能有效捕捉像素点的细节特征，对不规则、噪声干扰严重、边界模糊的细小血管有较好分割效果。
Vessel Image Segmentation Based on Multi-Directional Features and Connectivity Detection
Fundus images often contain a large number of small blood vessels with significant noise interference and blurred boundaries,making segmentation challenging.To address these characteristics,a fundus image segmentation method called MDF_Net&CD(Multi-Directional Features neural Network and Connectivity Detection)is proposed,based on multidirectional features and connectivity detection.A deep neural network model,MDF_Net(Multi-Directional Features neural Network),is designed to take different directional feature vectors of pixels as input.MDF_Net is used for the initial segmentation of the fundus images.A connectivity detection algorithm is proposed to revise the preliminary segmentation results of MDF_Net,according to the geometric characteristics of blood vessels.In the public fundus image dataset,MDF_Net&CD is compared with recent representative segmentation methods.The experimental results show that MDF_Net&CD can effectively capture the detailed characteristics of pixels,and has a good segmentation effect on irregular,severely noisy,and blurred boundaries of small blood vessels.The evaluation indices are balanced,and the sensitivity,F1 score,and accuracy are better than other methods participating in the comparison.
国家科技期刊平台
NSTL
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