This paper develops a new active contour (AC) model capable of multiple complex objects segmentation in the presence of heavy noise. The model segments images in the framework of two types of partial differential equations (PDEs): the Euler–Lagrange and Poisson PDEs. The former is used to build an evolution algorithm, while the Poisson solution gradient vector field (PGVF) directs the evolution toward the boundaries of all image objects. The AC halts on boundaries and PGVF separatrices, splits on the latter, and leaves at least one segment (called label) on every boundary. Each label tracks its boundary until the corresponding object is enveloped. The advantages of the new method are validated on a number of skin lesions, road, and aircraft images of varying sizes and in the presence of Gaussian noise. The obtained results are compared against results by contemporary and established active contours and neural networks.

本文开发了一种新的主​​动轮廓（AC）模型，该模型能够在存在重噪声的情况下对多个复杂的对象进行分割。该模型在两种类型的偏微分方程（PDE）的框架内对图像进行细分：Euler-Lagrange和Poisson PDE。前者用于构建演化算法，而泊松解梯度矢量场（PGVF）将演化引向所有图像对象的边界。AC在边界和PGVF分离线上停止，在后者上分裂，并在每个边界上保留至少一个线段（称为标签）。每个标签跟踪其边界，直到相应的对象被包围为止。这种新方法的优势已在各种大小不等且存在高斯噪声的皮肤损伤，道路和飞机图像上得到验证。