CT image-based 3D inflorescence estimation of Chrysanthemum seticuspe

Area of Science:

• Plant Science
• Computational Biology
• Imaging Technology

Background:

• Understanding the 3D structure of plant organs is crucial for botanical research.
• Computed tomography (CT) offers non-destructive 3D imaging of plant structures.
• Manual analysis of CT data for plant organ structures is time-consuming and labor-intensive.

Purpose of the Study:

• To develop an automated method for detecting contact points between receptacles and florets in 3D plant inflorescence structures.
• To accurately determine the 3D arrangement of florets on the receptacle of *Chrysanthemum seticuspe*.
• To improve the efficiency and accuracy of analyzing plant organ 3D structures from CT data.

Main Methods:

• Utilized computed tomography (CT) to generate 3D volume data of *Chrysanthemum seticuspe* capitulum inflorescences.
• Developed an image recognition technique to automatically detect receptacles and florets from CT slice images.
• Integrated detection results with prior knowledge of contact point locations to enhance accuracy.
• Employed clustering techniques to estimate the 3D positions of detected contact points.

Main Results:

• The proposed automatic method achieved high accuracy in detecting contact points on CT slice images.
• The method successfully estimated the 3D positions of contact points through clustering.
• Sample-independent experiments demonstrated that the automated method performs comparably to sample-dependent approaches.
• The technique significantly reduces the labor involved in analyzing plant 3D structures.

Conclusions:

• The automated contact point detection method based on CT slice images is effective and accurate.
• This approach enhances the study of 3D plant organ structures, particularly floret arrangements.
• The method offers a more efficient and reliable alternative to manual analysis in botanical imaging research.