Image Registration Using an Ant Colony Foraging Algorithm with Mutual Information
Date Issued
2015
Date
2015
Author(s)
Lin, Ting-Xun
Abstract
Image registration is very important for a wide variety of image processing applications in engineering and medicine. It provides lots of image information for further analysis in many fields. There are many image registration methods being proposed. This thesis describes a new image registration algorithm using an ant colony optimization (ACO) approach. There are two fundamental properties in the proposed ACO process: the probabilistic transition and the pheromone update. We used the ACO algorithm to solve the direction and distance of advancement and combined linear interpolation to transform images. Thanks to the efficient ACO, complex calculation such as solving the Navier–Stokes partial differential equation is not necessary. The entropy condition in information theory was introduced to obtain interactive information in order to improve registration accuracy and reduce processing time. A wide variety of images including aerial images and medical images were used to evaluate this new method. Experimental results indicated that the proposed method efficiently performed registration and provided high accuracy. Comparing to the viscous fluid model method, our algorithm produced higher correlation coefficient scores but also spent less computation time. We believe that our algorithm is of potential in many image registration applications.
Subjects
Image registration
ant colony optimization
mutual information
magnetic resonance image
aerial image
Type
thesis
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