High-Dimensional Entropy Estimation for Finite Accuracy Data: R-NN Entropy Estimator

Abstract. We address the problem of entropy estimation for highdimensional finite-accuracy data. Our main application is evaluating high-order mutual information image similarity criteria for multimodal image registration. The basis of our method is an estimator based on k-th nearest neighbor (NN) distances, modified so that only distances greater than some constant R are evaluated. This modification requires a correction which is found numerically in a preprocessing step using quadratic programming. We compare experimentally our new method with k-NN and histogram estimators on synthetic data as well as for evaluation of mutual information for image similarity.

• Type: Conference
• Year: 2007
• Where: IPMI
• Authors: Jan Kybic
• Keywords: Image Similarity Criteria, IPMI 2007, K-th Nearest Neighbor, Medical Imaging, Multimodal Image Registration