论文标题
基于像素关系的视网膜图像分割的基于像素关系
Pixel Relationships-based Regularizer for Retinal Vessel Image Segmentation
论文作者
论文摘要
图像分割的任务是根据适当的标签对图像中的每个像素进行分类。为图像分割提出了各种深度学习方法，以提供高精度和深度建筑。但是，深度学习技术在训练过程中使用像素损失功能。使用像素损失，忽略了网络学习过程中的像素邻居关系。像素的相邻关系是图像中必不可少的信息。利用相邻的像素信息比仅使用像素到像素信息的信息提供了优势。这项研究提出了将像素邻居关系信息提供给学习过程的正规化器。正规化器是通过图理论方法和拓扑方法构造的：通过图理论方法，图形laplacian用于利用基于输出图像和接地图像的分段图像的平滑度。通过拓扑方法，Euler特征用于识别和最小化分段图像上隔离对象的数量。实验表明，我们的方案成功捕获了像素邻居关系，并比没有正则化项的基线更好地提高了卷积神经网络的性能。
The task of image segmentation is to classify each pixel in the image based on the appropriate label. Various deep learning approaches have been proposed for image segmentation that offers high accuracy and deep architecture. However, the deep learning technique uses a pixel-wise loss function for the training process. Using pixel-wise loss neglected the pixel neighbor relationships in the network learning process. The neighboring relationship of the pixels is essential information in the image. Utilizing neighboring pixel information provides an advantage over using only pixel-to-pixel information. This study presents regularizers to give the pixel neighbor relationship information to the learning process. The regularizers are constructed by the graph theory approach and topology approach: By graph theory approach, graph Laplacian is used to utilize the smoothness of segmented images based on output images and ground-truth images. By topology approach, Euler characteristic is used to identify and minimize the number of isolated objects on segmented images. Experiments show that our scheme successfully captures pixel neighbor relations and improves the performance of the convolutional neural network better than the baseline without a regularization term.
