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 3R-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.