采用K均值聚类和环形结构的狭叶锦鸡儿木质部提取算法

针对木质部交互统计误差大、效率低、重现性差、劳动强度高和传统图像处理算法精度不理想等问题,该文以狭叶锦鸡儿木质部切片图像为研究对象,根据木质部特点提出基于K均值聚类算法和环形结构提取算法相结合,实现木质部准确提取的方法。首先通过动态巴特沃斯同态滤波法对30幅供试图像进行光照不均校正,然后采用K均值聚类法对光照补偿后图像初分割,最后采用环形结构提取算法实现木质部提取计数。试验结果表明:采用K均值聚类算法对光照补偿后的木质部图像初分割分割误差R(section error, R)、过分割误差OR(over-segmentation error, OR)和欠分割误差UR(under-segmentation error, UR)均值分别为5.15%、1.48%和6.46%,优于未光照补偿和3R-G-B算法;该文提出的环形结构提取算法对初分割后木质部图像检测的平均相对误差为2.26%,比分水岭法低11.69个百分点,比凹点匹配法低4.93个百分点。从速度上看,该算法平均耗时3.17 s,比分水岭法快1.40 s,比凹点匹配法快4.88 s。该算法检测的均方根误差RMSE(root mean squared error, RMSE)为0.52%,约相当于分水岭法的1/3,约相当于凹点匹配法的1/2,该算法优于其他2种分割算法;在图像结构复杂、光照不均匀、内部分布不均等缺陷条件下,该文算法也能很好地实现木质部的分割和提取。该方法不仅能对狭叶锦鸡儿木质部自动分割和提取,也可为其他植物木质部分割提取提供参考。

An extraction xylem images of Caragana stenophylla Pojark based on K-means clustering and circle structure extraction algorithm

In the slice images of the xylem of Caragana stenophylla Pojarkthis paper proposed a novel algorithm that combined the K-means clustering and circle structure extraction algorithm, to achieve much more accurate information data of the xylem than that from the traditional image processing algorithms. Firstly, the dynamic Butterworth homomorphic filtering can be used to compensate for illumination variations on V components in the 30 images of Caragana stenophylla Pojark xylem in a HSV color space;then the K-means clustering can be used to initially segment the a and b components of the pre-processed xylem images under the Lab color space with a cluster of 3,finally, the circle structure extraction algorithm can be used to accurately cluster and extract the specific feature of the xylem images. The processing results showed that the Butterworth homomorphic filtering have a good effect on the illumination compensation for the various illumination variations in a series of different images, indicating some high resolution information in detail, texture, contrast and visual effect of the images. After being initially segmented by K-means clustering, the illumination compensated xylem images had an average section error(R) of 5.15%, over-segmentation error(OR) of 1.48% and under-segmentation error(UR) of 6.46%, respectively, which decreased by 23.60, 7.75 and 13.01 percentage points, respectively compared to the xylem images before the illumination compensation. The segmentation accuracy was enhanced significantly, which decreased 10.43 percentage points in R, 4.58 percentage points in OR and 4.96 percentage points in UR to 3 R-G-B threshold value clustering algorithm after the illumination compensation. The average mean error of the circle structure extraction for the xylem images after the initial segment reached 2.26%, which was 11.69 percentage points lower than that of the watershed method, and 4.93 percentage points lower than that of pit matching method. The average duration of the algorithm in this case was 3.66 s on each image, saving 0.95 and 4.78 s compared to that of the watershed and pit matching method, respectively. The root mean squared error(RMSE) of the algorithm was 0.52%, one third of that from the watershed and half of that from the pit matching. The proposed combined algorithm can automatically segment and extract the xylem information data from Caragana stenophylla Pojark, particularly on some images with the complex xylem structure, uneven illumination and uneven internal distribution, indicating better than the other two types of segmentation algorithms. These findings can provide fundamental reference for the promising extraction algorithm and the image processing of the xylem from other plants.