Max-Index Based Local Self-Similarity Descriptor for Robust Multi-Modal Image Registration

In order to address problems, such as radiation and intensity differences in multi-modal images, this letter proposes a novel idea that integrates maximal indices into the construction of a local self-similarity (LSS) descriptor. The LSS vectors at the same angles but different radial intervals are added to construct the max-index similarity map (MISM) and form the proposed descriptor. This novel descriptor is named max-index-based local self-similarity (MLSS). The MLSS descriptor not only captures the shape similarity between images but is also robust to radiation distortions. Furthermore, a fast and robust algorithm is introduced based on the MLSS descriptor. Comprehensive analysis of accuracy, precision, and computational efficiency shows that the proposed method outperforms five other state-of-the-art methods with stable and better performance on nine pairs of multi-modal test images.