[论文解读] A new automatic approach to seed image analysis: From acquisition to segmentation

Alessandra Mendes Pacheco Guerra Vale, Mariano Ucchesu+4 more|arXiv (Cornell University)|Dec 11, 2020

Smart Agriculture and AI参考文献 30被引用 7

一句话总结

本论文提出了一款开源的、基于Java的ImageJ插件，用于全自动种子图像分割，可实现从扫描种子样本中快速且准确地提取形态学和色彩特征。在120种豆科植物的3,386粒种子上进行测试，该插件每张图像的分割准确率达到100%，耗时仅0.02秒——显著优于在四种蓝色背景条件下的手动分割方法（每张图像耗时63秒）。

ABSTRACT

Image Analysis offers a new tool for classifying vascular plant species based on the morphological and colorimetric features of the seeds, and has made significant contributions in systematic studies. However, in order to extract the morphological and colorimetric features, it is necessary to segment the image containing the samples to be analysed. This stage represents one of the most challenging steps in image processing, as it is difficult to separate uniform and homogeneous objects from the background. In this paper, we present a new, open source plugin for the automatic segmentation of an image of a seed sample. This plugin was written in Java to allow it to work with ImageJ open source software. The new plugin was tested on a total of 3,386 seed samples from 120 species belonging to the Fabaceae family. Digital images were acquired using a flatbed scanner. In order to test the efficacy of this approach in terms of identifying the edges of objects and separating them from the background, each sample was scanned using four different hues of blue for the background, and a total of 480 digital images were elaborated. The performance of the new plugin was compared with a method based on double image acquisition (with a black and white background) using the same seed samples, in which images were manually segmented using the Core ImageJ plugin. The results showed that the new plugin was able to segment all of the digital images without generating any object detection errors. In addition, the new plugin was able to segment images within an average of 0.02 s, while the average time for execution with the manual method was 63 s. This new open source plugin is proven to be able to work on a single image, and to be highly efficient in terms of time and segmentation when working with large numbers of images and a wide diversity of shapes.

研究动机与目标

• 解决在自动化植物系统学研究中准确且高效分割种子图像的挑战。
• 开发一种完全自动化的开源解决方案，兼容ImageJ，用于高通量种子图像分析。
• 在不同背景条件下，评估该方法在多样化种子形状和颜色下的性能表现。
• 从准确率和速度两方面，将新方法与手动分割及双图像采集技术进行比较。
• 实现从种子图像中大规模提取形态学和色彩特征，以支持分类学研究。

提出的方法

• 开发用于ImageJ的自定义Java插件，以实现种子图像分割的自动化。
• 使用不同色调的蓝色背景平板扫描仪进行图像采集，以测试方法的鲁棒性。
• 应用自动阈值分割和边缘检测算法，将种子从背景中分离。
• 采用双图像采集方法（黑白背景）作为手动基准进行对比。
• 实现一致的分割流程，适用于单张图像且无需用户干预。
• 在120种豆科植物的480张图像上评估分割准确率和处理时间。

实验结果

研究问题

• RQ1 自动化开源插件是否能在多样化种子形状和颜色下实现100%的分割准确率？
• RQ2 与使用Core ImageJ的手动分割相比，新插件的处理速度如何？
• RQ3 背景颜色选择（蓝色色调）是否显著影响分割性能？
• RQ4 在高通量环境下，该插件能否可靠地完成分割且不产生目标检测错误？
• RQ5 该方法在从复杂或均匀背景中分离种子方面效果如何？

主要发现

• 该插件在全部480张测试图像中均实现了100%的分割准确率，未出现任何目标检测错误。
• 平均分割时间为每张图像0.02秒，远低于手动分割的每张63秒。
• 插件在所有四种蓝色背景色调下表现一致，证明其对背景颜色变化具有强鲁棒性。
• 该方法无需用户输入，即可成功分割豆科植物中形状和大小多样的种子。
• 该插件在大规模图像分析中表现出极高效率，可实现对数千张种子图像的快速处理。
• 结果证实，自动化分割不仅可行，而且显著快于且更可靠于人工方法。