Scatter Search for the 3D Point Matching Problem in Image Registration

Scatter search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. In this paper, we present a scatter search implementation designed to find high quality solutions for the 3D image registration problem, which has a significant number of applications in practice. This problem arises in computer vision applications when finding a correspondence or transformation between two computer images taken under different conditions. Our implementation goes beyond a simple exercise on applying scatter search, by incorporating innovative mechanisms to combine and improve solutions and to create a balance between intensification and diversification in the reference set. Besides, heuristic information taken from a preprocessing of the images is incorporated into the algorithm in order to improve its performance. Our computational experimentation in a real-world medical registration application establishes the effectiveness of the scatter search procedure in relation to different approaches usually applied to solve the problem.