阈值标记的分水岭彩色图像分割

Watershed algorithm with threshold mark for color image segmentation

目的针对传统分水岭算法中产生的过分割问题,提出一种基于阈值标记的分水岭彩色图像分割算法。方法该方法将分水岭算法直接应用到原始梯度图像上而不是简化之后的图像,这样做的目的是可以保护边缘信息不受损失;利用不同尺寸结构元求取彩色图像形态学梯度,解决了关于保护边缘和图像简化之间的矛盾。同时算法设计一种阈值自动选取与标记提取方法,从梯度的低频成分中提取与物体相关的局部极小值,用这些极小值构成的二值图像强制标定原始梯度图像,在修改后的梯度上进行分水岭分割。结果在仿真实验中,利用本文算法针对不同RGB彩色图像进行分割,获得准确、连续封闭的分割边界,与其他同类方法相比,得到符合人类视觉的最小分割区域数,同时在运行效率上也有很大提高。结论该方法可以自适应提取标记而不需要先验知识,有效解决了分水岭算法的过分割问题,相对于传统的算法,提高了分割性能,有较好的适用性和鲁棒性,可将其应用于机器视觉、生物医学以及高光谱遥感图像分割领域。

Objective In view of the segmentation problem caused by the traditional watershed algorithm, a new improved watershed algorithm for color image segmentation based on threshold mark is proposed. Method The method applied water- shed algorithm directly to the original gradient image but not the simplified image, the purpose of which is to insure the loss edge information can be avoided. With different size structure element for color morphological gradient image, solved the contradiction between the protection of the edge and the image is simplified. At the same time, the new algorithm designed a method of threshold automaticly selection and marker extraction-extracting local minimum value related to the objects from the low-frequency components of the gradient, with these minimal values constituting the binary image force calibration of the original gradient image, then used watershed segmentation on the modified gradient. Result In the simulation experi- ment, the algorithm in this paper is compared with similar segmentation method. Algorithm obtained accurate and continu- ous closed boundary for the different RGB color images segmented, which got the minimum segmentation number that con- forming to human vision, and improved the working efficiency. Conclusion The method can adaptively extract marker with-out the need for prior knowledge, effectively solved the over segmentation problem of watershed, compared with the tradi- tional algorithm improved the segmentation performance and having good applicability and robustness. It can be applied to the machine vision, biomedicine and hyperspectral remote sensing image segmentation.