CT image-based 3D inflorescence estimation of Chrysanthemum seticuspe

Soushi Matsumoto, Yuzuko Utsumi*, Toshiaki Kozuka, Masakazu Iwamura, Tomonori Nakai, Daisuke Yamauchi, Ichirou Karahara, Yoshinobu Mineyuki, Masato Hoshino, Kentaro Uesugi, Koichi Kise

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.

Original languageEnglish
Article number1374937
JournalFrontiers in Plant Science
Volume15
DOIs
StatePublished - 2024

Keywords

  • 3D data analysis
  • clustering
  • CT image
  • object detection
  • segmentation

ASJC Scopus subject areas

  • Plant Science

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