Image Registration and Segmentation by Maximizing the Jensen-Rényi Divergence
نویسندگان
چکیده
Information theoretic measures provide quantitative entropic divergences between two probability distributions or data sets. In this paper, we analyze the theoretical properties of the Jensen-Rényi divergence which is defined between any arbitrary number of probability distributions. Using the theory of majorization, we derive its maximum value, and also some performance upper bounds in terms of the Bayes risk and the asymptotic error of the nearest neighbor classifier. To gain further insight into the robustness and the application of the Jensen-Rényi divergence measure in imaging, we provide substantial numerical experiments to show the power of this entopic measure in image registration and segmentation.
منابع مشابه
A generalized divergence measure for robust image registration
Entropy-based divergence measures have shown promising results in many areas of engineering and image processing. In this paper, we define a new generalized divergence measure, namely, the Jensen–Rényi divergence. Some properties such as convexity and its upper bound are derived. Based on the Jensen–Rényi divergence, we propose a new approach to the problem of image registration. Some appealing...
متن کاملAn Information Divergence Measure for Isar Image Registration
Entropy-based divergence measures have shown promising results in many areas of engineering and image processing. In this paper, a generalized information-theoretic measure called Jensen-Rényi divergence is proposed. Some properties such as convexity and its upper bound are derived. Using the Jensen-Rényi divergence, we propose a new approach to the problem of ISAR (Inverse Synthetic Aperture R...
متن کاملAssessment of the Log-Euclidean Metric Performance in Diffusion Tensor Image Segmentation
Introduction: Appropriate definition of the distance measure between diffusion tensors has a deep impact on Diffusion Tensor Image (DTI) segmentation results. The geodesic metric is the best distance measure since it yields high-quality segmentation results. However, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. The main goal of this ...
متن کاملDivergence Measures for Dna Segmentation
Entropy-based divergence measures have shown promising results in many areas of engineering and image processing. In this study, we use the Jensen-Shannon and Jensen-Rényi divergence measures for DNA segmentation. Based on these information theoretic measures and protein shape coded in DNA, we propose a new approach to the problem of finding the borders between coding and noncoding DNA regions....
متن کاملDecision making in medical investigations using new divergence measures for intuitionistic fuzzy sets
In recent times, intuitionistic fuzzy sets introduced by Atanassov has been one of the most powerful and flexible approaches for dealing with complex and uncertain situations of real world. In particular, the concept of divergence between intuitionistic fuzzy sets is important since it has applications in various areas such as image segmentation, decision making, medical diagnosis, pattern reco...
متن کامل