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Automatic hierarchy classification in venation networks using directional morphological filtering for hierarchical structure traits extraction

文献类型： 外文期刊

作者： Gan, Yangjing 1 ; Rong, Yi 1 ; Huang, Fei 1 ; Hu, Lun 1 ; Yu, Xiaohan 1 ; Duan, Pengfei 1 ; Xiong, Shengwu 1 ; Liu, Haipi 1 ;

作者机构： 1.Wuhan Univ Technol, Dept Comp & Sci, Luoshi Rd 122, Wuhan, Hubei, Peoples R China

2.Tibet Acad Agr & Anim Husb Sci, Inst Fisheries Sci, Tibet, Peoples R China

关键词： Vein order classification; Venation network images; Vein traits extraction; Direction morphological filtering

期刊名称：COMPUTATIONAL BIOLOGY AND CHEMISTRY （ 影响因子：2.877； 五年影响因子：2.411 ）

ISSN： 1476-9271

年卷期： 2019 年 80 卷

页码：

收录情况： SCI

摘要： The extraction of vein traits from venation networks is of great significance to the development of a variety of research fields, such as evolutionary biology. However, traditional studies normally target to the extraction of reticulate structure traits (ReSTs), which is not sufficient enough to distinguish the difference between vein orders. For hierarchical structure traits (HiSTs), only a few tools have made attempts with human assistance, and obviously are not practical for large-scale traits extraction. Thus, there is a necessity to develop the method of automated vein hierarchy classification, raising a new challenge yet to be addressed. We propose a novel vein hierarchy classification method based on directional morphological filtering to automatically classify vein orders. Different from traditional methods, our method classify vein orders from highly dense venation networks for the extraction of traits with ecological significance. To the best of our knowledge, this is the first attempt to automatically classify vein hierarchy. To evaluate the performance of our method, we prepare a soybean transmission image dataset (STID) composed of 1200 soybean leaf images and the vein orders of these leaves are manually coarsely annotated by experts as ground truth. We apply our method to classify vein orders of each leaf in the dataset. Compared with ground truth, the proposed method achieves great performance, while the average deviation on major vein is less than 5 pixels and the average completeness on second-order veins reaches 54.28%.

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