Image Registration Using Mutual Information as Similarity Metric

During the registration process, mutual information can be utilized as a metric to evaluate the similarity between two images. Mutual information quantitatively measures the statistical dependence and shared information content between images, making it particularly effective for multimodal registration where intensity relationships may be non-linear. The implementation typically involves calculating the joint probability distribution of pixel intensities from both images and computing the entropy-based metric using the formula: MI(A,B) = H(A) + H(B) - H(A,B), where H represents entropy. Through mutual information maximization using optimization algorithms like gradient descent or simplex methods, we can determine the optimal transformation parameters (such as rotation, translation, and scaling) that align the images. This approach enables robust comparison and matching of different medical or satellite images, even when they come from different imaging modalities. Therefore, in this methodology, we employ mutual information as our primary registration criterion to ensure effective alignment and meaningful comparison between diverse image datasets.