文献类型：期刊文献

英文题名：A computer-aided method for identifying the presence of softwood growth ring boundaries

作者：Lin, Qizhao[1] He, Tuo[2,3] Sun, Yongke[1] He, Xin[1] Qiu, Jian[1]

第一作者：Lin, Qizhao

通信作者：Lin, QZ[1];Qiu, J[1]

机构：[1]Southwest Forestry Univ, Coll Mat Sci & Engn, Kunming, Yunnan, Peoples R China;[2]Chinese Acad Forestry, Res Inst Wood Ind, Beijing, Peoples R China;[3]Chinese Acad Forestry, Wood Collect WOODPEDIA, Beijing, Peoples R China

年份：2020

卷号：15

期号：9

外文期刊名：PLOS ONE

收录：;Scopus(收录号：2-s2.0-85091324053);WOS:【SCI-EXPANDED(收录号:WOS:000573851100010)】；

基金：This work was supported by the National Keypoint Research and Invention Program in 13th Five-Year, China, No. 2016YFD0600702 to JQ.

语种：英文

摘要：The objective of this study was to develop a computer-aided method to quantify the obvious degree of growth ring boundaries of softwood species, based on data analysis with some image processing technologies. For this purpose, a 5x magnified cross-section color micro-image of softwood was cropped into 20 sub-images, and then every image was binarized as a gray image according to an automatic threshold value. After that, the number of black pixels in the gray image was counted row by row and the number of black pixels was binarized to 0 or 100. Finally, a transition band from earlywood to latewood on the sub-image was identified. If everything goes as planned, the growth ring boundaries of the sub-image would be distinct. Otherwise would be indistinct or absent. If more than 50% sub-images are distinct, with the majority voting method, the growth ring boundaries of softwood would be distinct, otherwise would be indistinct or absent. The proposed method has been visualized as a growth-ring-boundary detecting system based on the .NET Framework. A sample of 100 micro-images (see S1 Fig via https://github.com/senly2019/Lin-Qizhao/) of softwood cross-sections were selected for evaluation purposes. In short, this detecting system computes the obvious degree of growth ring boundaries of softwood species by image processing involving image importing, image cropping, image reading, image grayscale, image binarization, data analysis. The results showed that the method used avoided mistakes made by the manual comparison method of identifying the presence of growth ring boundaries, and it has a high accuracy of 98%.