Strisciuglio Nicola, Azzopardi George, Petkov Nicolai
IEEE Trans Image Process. 2019 Dec;28(12):5852-5866. doi: 10.1109/TIP.2019.2922096. Epub 2019 Jun 21.
Delineation of curvilinear structures in images is an important basic step of several image processing applications, such as segmentation of roads or rivers in aerial images, vessels or staining membranes in medical images, and cracks in pavements and roads, among others. Existing methods suffer from insufficient robustness to noise. In this paper, we propose a novel operator for the detection of curvilinear structures in images, which we demonstrate to be robust to various types of noise and effective in several applications. We call it RUSTICO, which stands for RobUST Inhibition-augmented Curvilinear Operator. It is inspired by the push-pull inhibition in visual cortex and takes as input the responses of two trainable B-COSFIRE filters of opposite polarity. The output of RUSTICO consists of a magnitude map and an orientation map. We carried out experiments on a data set of synthetic stimuli with noise drawn from different distributions, as well as on several benchmark data sets of retinal fundus images, crack pavements, and aerial images and a new data set of rose bushes used for automatic gardening. We evaluated the performance of RUSTICO by a metric that considers the structural properties of line networks (connectivity, area, and length) and demonstrated that RUSTICO outperforms many existing methods with high statistical significance. RUSTICO exhibits high robustness to noise and texture.
图像中曲线结构的描绘是多个图像处理应用的重要基础步骤，例如航空图像中道路或河流的分割、医学图像中血管或染色膜的分割以及人行道和道路中的裂缝检测等。现有方法对噪声的鲁棒性不足。在本文中，我们提出了一种用于检测图像中曲线结构的新型算子，我们证明它对各种类型的噪声具有鲁棒性，并且在多个应用中有效。我们将其称为RUSTICO，它代表鲁棒抑制增强曲线算子。它受到视觉皮层中推拉抑制的启发，并将两个极性相反的可训练B-COSFIRE滤波器的响应作为输入。RUSTICO的输出包括一个幅度图和一个方向图。我们在一个合成刺激数据集上进行了实验，该数据集的噪声来自不同分布，以及在几个视网膜眼底图像、裂缝路面和航空图像的基准数据集以及一个用于自动园艺的玫瑰丛新数据集上进行了实验。我们通过一种考虑线网络结构属性（连通性、面积和长度）的度量来评估RUSTICO的性能，并证明RUSTICO在统计上具有高度显著性地优于许多现有方法。RUSTICO对噪声和纹理表现出高鲁棒性。
