Facial plastic surgery area acquisition method based on point cloud mathematical model solution

Area of Science:

• Medical Engineering
• Computational Geometry
• Plastic Surgery

Background:

• Accurate facial plastic surgery area acquisition is crucial for obtaining sufficient skin grafts for extensive wounds.
• Current methods using laser scanning and 3D reconstruction are accurate but struggle with random errors and computation time.
• There is a need for a faster, more efficient, and reliable method for facial area measurement in plastic surgery.

Purpose of the Study:

• To propose and evaluate a novel point cloud mathematical model solution for acquiring facial plastic surgery areas.
• To improve the accuracy and efficiency of facial area computation compared to existing methods.
• To provide a reliable method for determining the precise skin source needed for reconstructive surgery.

Main Methods:

• Point cloud data pretreatment and symmetric treatment.
• Generation of a color difference map to identify facial area.
• Development of a slicing mathematical model for area calculation.
• Direct computation of the facial plastic surgery area using point cloud data.

Main Results:

• The proposed method accurately and efficiently computes the facial plastic surgery area.
• Symmetric treatment of point cloud data helps in error assessment.
• The slicing mathematical model effectively defines the surgical area.
• Comparative analysis confirmed the method's effectiveness.

Conclusions:

• The point cloud mathematical model solution provides an accurate and efficient method for facial plastic surgery area acquisition.
• This technique addresses the limitations of current methods regarding error control and computation time.
• The developed approach is effective for determining precise skin graft requirements in reconstructive surgery.