基于自适应结构元素的改进分水岭图像分割方法

图像分割是按照一定的规则，将图像中具有特殊意义的区域划分为若干个互不相交的子区域，是从图像处理到图像分析的关键环节，传统分水岭图像分割方法是一种应用较为广泛的技术，具有快速、简单的优点，但该方法易受噪声干扰，分割结果易丢失边缘重要信息，出现过分割现象。为改善传统分水岭图像分割方法存在的过分割问题，提出了一种基于自适应结构元素的改进分水岭图像分割方法。首先，利用图像像素点邻域的局部密度、对称度及边缘特征构造形状可变的自适应结构元素，确保其与图像目标几何结构具有较强的一致性；其次，利用该结构元素获取图像形态学梯度，提高目标边缘的定位精度；将L₀范数梯度最小化和形态学开闭混合重建相结合修正梯度图像，减少梯度图像中的局部无效最小值点，抑制过分割现象的产生；最后对修正后的梯度图像进行分水岭分割，实现图像目标区域的精确分割。实验结果表明，该方法能够有效抑制过分割现象，提高目标边缘定位的准确性，具有较高的分割精度。

Improved watershed image segmentation method based on adaptive structural elements

Image segmentation is to divide the region with special meanings into several disjoint sub-regions according to certain rules, which is the key link between image processing and image analysis. The traditional watershed image segmentation method is widely used, which has the advantages of fast and simple. However, it is easily interfered by noise, and the segmentation results are prone to lose important edge information, resulting in over-segmentation. In view of the problem of the traditional watershed image segmentation method, an improved watershed image segmentation method based on adaptive structural elements was proposed. First, the adaptive structural elements with variable shapes were constructed by using local density, symmetry, and boundary features of adjacent pixels of image targets, so as to ensure a good consistency between the proposed structural elements and the shape of image targets. Then, the adaptive structural elements were used to obtain the morphological gradient of the image, which could improve the positioning accuracy of the target edge. The L₀ norm gradient minimization and morphological open-close hybrid reconstruction were used to modify the gradient image, so as to reduce the local invalid minimum points in the gradient image and suppress the occurrence of over-segmentation. Finally, watershed segmentation was performed on the modified gradient image to realize accurate segmentation of the target region of the image. Experimental results show that the method could effectively restrain over-segmentation of traditional watershed algorithm and improve the accuracy of the target edge positioning, with high precision of image segmentation.