基于形态学尺度空间和梯度修正的分水岭分割

分水岭是一种有效的图像分割方法，但存在过分割现象，为此提出了一种基于形态学尺度空间和梯度修正的分水岭图像分割方法，该方法利用形态学混合开闭重建尺度空间和梯度修正技术，在平滑原始图像的同时保留了重要的区域轮廓而去除了易造成过分割的区域细节和噪声，克服了传统的形态学开闭尺度空间在平滑细节和噪声时，部分重要区域轮廓也被平滑及不满足尺度因果性的问题。对平滑后的图像采用梯度修正分水岭变换，保持了尺度和分割区域数目间的因果性，进一步消除了标准分水岭的过分割现象。仿真实验表明，该方法能有效地消除过分割现象，分割的区域数目满足尺度因果性，且具有较高的区域轮廓定位能力。

Watershed Segmentation Based on Morphological Scale-Space and Gradient Modification

A method for watershed image segmentation based on morphological scale-space and gradient modification is proposed to avoid over-segmentation and the drawbacks of some improved watershed segmentations. Firstly, morphological hybrid opening and closing by reconstruction scale-space is employed to smooth the original image, after smoothing, the essential region contours are preserved and unimportant details and noise which are often the causes of over-segmentation are removed, and the problem of the traditional morphological opening and closing scale-space, including the lost of partial essential region contours and not satisfying scale causality, are both avoided. Secondly, in order to eliminate over-segmentation and to keep the scale causality from the extreme to the segmented regions, gradient modification is used before the standard watershed transform, to remove the regional minimum in the gradient image caused by the regional maximum in the smoothed image. Simulations show that this method can efficiently not only avoid over-segmentation, but also satisfy scale causality, and the localization of region contours is precise.