改进自适应分水岭方法分割棉花叶部粘连病斑

针对棉花叶部病斑相互之间存在粘连问题,该文提出了一种自适应分水岭分割方法。该方法在H-minima分水岭分割方法基础上,结合最小二乘圆法误差理论,对图像中每个连通分量进行最小二乘圆拟合,并计算最小二乘圆误差值,通过最小二乘圆误差值大小判断每个连通分量的轮廓不规则度,针对不同轮廓不规则度确定H-minima变换的极小值阈值,根据不同极小值阈值实现棉花叶部粘连病斑的分水岭分割。不同数量粘连病斑分割试验结果表明:该方法实现了棉花叶部粘连病斑数量从2个粘连至5个粘连病斑的自动分割,分割准确率为91.25%,平均运行时间为0.088 s。不同分割方法对比结果显示:该方法能实现对棉花轮纹病、褐斑病、炭疽病、叶斑病和棉铃疫病共5种病害的粘连病斑自动分割,并将距离分水岭分割方法、梯度分水岭分割方法、标记分水岭分割方法、Chan-Vese方法、高斯混合方法与该文方法比较,正确分割率分别为67.8%、36.4%、83.7%、70.3%、82.1%、93.5%,该方法优于其他5种分割方法,有效抑制了过分割问题;在复杂背景、光照不均匀、病斑大小不一致等复杂条件下,该文方法也能较好地实现粘连病斑的分割。该方法不仅能对棉花叶部粘连病斑自动分割,也能为其他作物叶片粘连病斑分割提供参考。

Improved adaptive watershed method for segmentation of cotton leaf adhesion lesions

Accurate segmentation of cotton leaf adhesion lesions not only can extract the feature vector of individual lesions to a large extent, but also is important for the improvement of the accuracy of lesion identification. Some methods for adhesion lesions segmentation may result in over-segmentation or under-segmentation. In addition, some segmentation methods for adhesion lesions can better solve the segmentation problem, but the selection of parameters is sensitive, which needs to be manually set and cannot adapt to complex conditions, such as H-minima transform. Aiming at the adhesion problem between lesions, an adaptive watershed segmentation method was proposed. Firstly, for the cotton lesion area extraction, the Gaussian filter was used for image filtering processing, and then the super green color component was extracted and OTSU binary segmentation was performed. Post-segmentation processing was carried by the mathematical morphology of hole filling and the morphological opening operation, so that the contour of segmented lesion area was continuous and the edge was smooth. Secondly, the local minimum threshold (h) was determined. Based on the H-minima watershed segmentation method, the proposed method combined the least squares method error theory to fit the least squares of each connected component in the image. And the least squares error value was calculated, then the contour irregularity of each connected component was determined according to the least squares error value. The minimum threshold h of the H-minima transform was determined based on different contour irregularities. Finally, Watershed segmentation of cotton leaf adhesion lesions was achieved based on different minimum thresholds. A total of 160 images with 2 to 5 adhesion lesions were selected from the lesion sample library for testing. The lesions segmentation accuracy for cotton leaves with different number of adhesion lesions was 91.25% with running time of 0.088 s. The proposal method achieved the automatic segmentation , and was especially suitable for the adhesion of different lesions and irregularities. Meanwhile, 150 samples with high degree adhesion lesion of ring disease, brown spot, anthracnose, leaf spot and cotton boll blight were selected for different segmentation methods contrast test. The results of contrast test - showed that the proposed method could automatically segment the adhesion lesions of 5 diseases. Distance watershed method, Gradient watershed segmentation method, marker watershed segmentation method, Chan-Vese method, Gaussian mixture method and the proposed method, the correct segmentation ratio were 67.8%, 36.4%, 83.7%, 70.3%, 82.1% and 93.5%, respectively, with the average running time of 0.034, 0.036, 0.046, 0.357, 0.108 and 0.094 s. The experimental results showed that the proposed method was superior to the other 5 methods, especially the over-segmentation problem was effectively suppressed. The proposed method took slightly longer time than the distance watershed segmentation method, the gradient watershed segmentation method, and the marker watershed segmentation method, and was lower than the Chan-Vese method and the Gaussian mixture method, which could still meet the real-time image processing requirements. The results of lesion segmentation test in complex environment showed that under complex conditions such as complex background, uneven illumination and uniform lesion size, the proposed method could better achieve the segmentation of adhesion lesions, and its segmentation accuracy and running time could meet the actual needs. The proposed method can not only automatically segment the adhesion lesions on cotton leaf, but also provide reference for the segmentation of adhesion lesions for other crop leaves.