Collision and containment detection between biomechanically based eye muscle volumes

Area of Science:

• Computational geometry
• Medical simulation
• Computer graphics

Background:

• Collision detection is vital for 3D simulation systems.
• Existing methods often struggle with deformable bodies.
• Ophthalmology software requires accurate simulation of eye muscles.

Purpose of the Study:

• To develop and evaluate methods for collision detection between deformable 3D eye muscle models.
• To address the challenge of collisions arising from projecting surface models onto biomechanical paths.
• To provide a foundation for collision response in ophthalmologic simulations.

Main Methods:

• Implemented three distinct collision detection approaches.
• Method 1: Image-space techniques utilizing OpenGL.
• Method 2: Leveraging the Bullet physics library.
• Method 3: Optimized space-array data structure with software rendering.

Main Results:

• Successfully implemented and compared three collision detection techniques for deformable eye muscle models.
• Demonstrated the feasibility of detecting collisions in a specific ophthalmology diagnostic software context.
• Provided a basis for future collision response mechanisms.

Conclusions:

• The presented methods offer viable solutions for collision detection in simulations involving deformable bodies.
• The study contributes to advancing the accuracy and capabilities of ophthalmologic diagnostic software.
• Further research into collision response strategies is warranted.