Energy minimization in medical image analysis: Methodologies and applications

Area of Science:

• Medical Image Analysis
• Computational Optimization

Background:

• Energy minimization is crucial for medical image analysis.
• Numerous optimization schemes have been developed over the past two decades.
• A comprehensive survey of state-of-the-art optimization approaches is needed.

Purpose of the Study:

• To provide a comprehensive survey of state-of-the-art energy minimization optimization approaches in medical image analysis.
• To classify these methods into continuous and discrete categories.
• To review comparative studies and applications.

Main Methods:

• Classification of optimization algorithms into continuous (e.g., Newton-Raphson, gradient descent) and discrete (e.g., graph cuts, belief propagation) methods.
• Discussion of specialized methods like minimal surface, primal-dual, and multi-objective optimization.
• Review of comparative studies evaluating accuracy, efficiency, and complexity.

Main Results:

• Detailed categorization of various continuous and discrete energy minimization techniques.
• Overview of comparative studies assessing the performance of different optimization methods.
• Identification of key medical image analysis applications benefiting from these techniques.

Conclusions:

• Energy minimization techniques are diverse, falling into continuous and discrete categories.
• Comparative studies are essential for understanding method performance.
• These optimization methods are vital for numerous medical imaging applications such as segmentation, registration, and reconstruction.