Due to the influence of imaging equipment and complex imaging environments, most images in daily life have features of intensity inhomogeneity and noise. Therefore, many scholars have designed many image segmentation algorithms to address these issues. Among them, the active contour model is one of the most effective image segmentation algorithms.This paper proposes an active contour model driven by the hybrid signed pressure function that combines global and local information construction. Firstly, a new global region-based signed pressure function is introduced by combining the average intensity of the inner and outer regions of the curve with the median intensity of the inner region of the evolution curve. Then, the paper uses the energy differences between the inner and outer regions of the curve in the local region to design the signed pressure function of the local term. Combine the two SPF function to obtain a new signed pressure function and get the evolution equation of the new model. Finally, experiments and numerical analysis show that the model has excellent segmentation performance for both intensity inhomogeneous images and noisy images.

翻译：受成像设备及复杂成像环境影响，日常生活中的图像大多具有强度不均匀和噪声特征。为此，众多学者设计了多种图像分割算法以解决上述问题，其中主动轮廓模型是最有效的图像分割算法之一。本文提出一种结合全局与局部信息构建的混合符号压力函数驱动的主动轮廓模型。首先，通过结合曲线内外区域的平均强度与演化曲线内部区域的中值强度，引入一种新的基于全局区域的符号压力函数。接着，利用局部区域中曲线内外区域的能量差异设计局部项的符号压力函数。将两个SPF函数相结合，获得新的符号压力函数及新模型的演化方程。最后，实验与数值分析表明，该模型对强度不均匀图像和噪声图像均具有优异的分割性能。