大豆叶片图像的叶脉分割方法

叶脉分割是叶片模式分析的一个重要步骤, 对大豆的品种识别、表型研究具有十分重要的意义. 由于大豆叶脉结构十分复杂, 叶脉所在叶片区域的低对比度, 只借助灰度信息分割叶脉一般无法取得理想的分割效果. 本文提出了一种结合多尺度灰度无约束击中或击不中变换 (UHMT) 算法和基于HSI颜色空间的色调信息处理方法的大豆叶脉分割方法. 该方法将RGB颜色空间中的灰度信息和HSI颜色空间中的色调信息, 分别用于大豆叶片图像的全局叶脉分割和局部一级、二级叶脉分割. 前者采用迭代阈值分割提取叶片区域, 通过膨胀腐蚀消除叶片外轮廓以及叶柄等干扰因素, 得到叶片区域图像, 然后, 运用多尺度灰度UHMT算法得到全局叶脉图像. 后者, 针对一级和二级叶脉分割效果差的问题, 使用色调信息扩大叶脉与其他像素点灰度值差异, 以实现局部一级、二级叶脉的分割. 将获得的全局叶脉和局部叶脉图像融合, 获得最终的大豆叶脉图像. 为验证算法的有效性, 本文使用了大豆品种叶片图像数据库SoyCultivar中的大豆叶片图像进行实验. 结果表明, 该方法比现有的叶脉分割方法好, 不仅能够完整地提取大豆叶脉, 而且能够很好地消除背景以及叶片外轮廓、叶柄等无关成分.

Leaf Vein Segmentation Method of Soybean Leaf Images

Leaf vein segmentation is an important step in leaf pattern analysis, which is of great significance for soybean variety identification and phenotype research. On account of the complicated vein structure of soybean leaves and the low contrast of the leaf area where the veins are located, it is generally impossible to achieve ideal leaf vein segmentation results only using gray information. This study presents a soybean vein segmentation method combining the multi-scale gray unconstrained hit-or-miss transform (UHMT) algorithm and the processing method based on the hue data of HSI color space. In this method, the gray information in RGB color space and the hue data in HSI color space are used to segment the global leaf veins and local primary and secondary veins from soybean leaf images, respectively. The former uses iterative threshold segmentation to extract the leaf area and eliminates interference factors such as the outer contour and the petiole through expansion and corrosion to obtain the leaf area image. Then, the multi-scale gray UHMT algorithm is employed to obtain the global leaf vein image. Considering the poor performance of primary and secondary vein segmentation, we use hue data to enlarge the discrepancies in gray values between veins pixels and other pixels to realize the segmentation of local primary and secondary veins. The obtained global and local vein images are fused into the final soybean leaf vein image. Moreover, this study utilizes soybean leaf images in the soybean leaf image database, SoyCultivar, to verify the effectiveness of the algorithm. The results indicate that this algorithm is better than existing leaf vein segmentation methods as it can not only extract soybean leaf veins completely but also well eliminate the background, leaf contours, petioles, and other irrelevant components.