Segmentation of medical image sequence by parallel active contour.

Area of Science:

• Medical Imaging
• Computer Vision
• Artificial Intelligence

Background:

• Object detection and tracking in medical image sequences are crucial for diagnosis and treatment planning.
• Traditional methods often face challenges with complex image dynamics and noise.
• Parametric active contour models (snakes) offer a robust approach but require efficient implementation strategies.

Purpose of the Study:

• To present an original multi-agent system (MAS) for detecting and tracking objects in medical image sequences.
• To implement a parametric contour active model (snake) using the NetLogo platform.
• To demonstrate the efficiency of the proposed MAS through experimental results.

Main Methods:

• A multi-agent system (MAS) was developed on the NetLogo platform.
• Mobile agents (turtles) represented points of the snake (snaxels), minimizing energy functionals in parallel.
• Stationary agents (patches) formed the image grid, with an Observer agent managing frames and initialization.

Main Results:

• The proposed MAS successfully implemented the parametric contour active model for object tracking.
• Experimental results demonstrated the system's efficiency in detecting and tracking objects within medical image sequences.
• The parallel processing capability of the MAS contributed to efficient snake energy minimization.

Conclusions:

• The developed multi-agent system provides an effective and efficient approach for object detection and tracking in medical imaging.
• The NetLogo platform is suitable for implementing complex image analysis models like parametric active contours.
• This novel MAS approach shows promise for improving automated analysis of medical image sequences.