基于分水岭算法的作物病害叶片图像分割方法

为了提高作物病害叶片图像分割的准确性,采用了一种改进的基于标记的分水岭图像分割算法。首先,通过对二值图像进行距离变换和分水岭分割来获取背景标记,并通过提取数学形态学重建后的梯度图像中的区域极小值得到初步的前景标记,接着对前景标记进行进一步过滤,消除部分伪前景标记;然后,通过强制极小值方法将背景标记和前景标记叠加在梯度图像上;最后,对修改后的梯度图像进行分水岭变换。采用该方法对多幅黄瓜病害叶片进行图像分割,实验结果表明:该方法能够较好地将病斑部分分割出来,分割结果不受叶片纹理的干扰,平均分割正确率能够达到90%以上,具有一定的有效性和实用价值。

Segmentation method for crop disease leaf images based on watershed algorithm

A new method based on watershed algorithm was proposed to raise the segmentation accuracy of the crop disease leaf images.At first,distance transformation and watershed segmentation were conducted on the binary crop disease leaf images to get the background marker,and the preliminary foreground markers were generated by extracting the regional minimum from the reconstructed gradient images,and then some fake foreground markers were eliminated by the further filter.In the next step,both background markers and foreground markers were imposed on the gradient image by the compulsive minimum algorithm.At last,the watershed transformation was carried out on the modified gradient image.Lots of cucumber disease leaf images were segmented effectively using the method.The results of experiment indicate that disease spots can be separated precisely from the crop leaf images.Additionally,the segmentation results are not influenced by leaf texture and its accuracy is up to more than 90 percent,so the method has certain validity and practical value.