An Unsupervised Multispectral Image Registration Network for Skin Diseases

Multispectral imaging has a broad, promising and advantageous application prospect in the diagnosis of skin diseases. However, there are inherent deviations such as rigid or non-rigid deformation among multi-spectral images (MSI), which makes accurate and robust registration algorithms desirable to extract reliable multispectral features. Existing registration algorithms are susceptible to significant and nonlinear amplitude differences and geometric distortions among MSI, resulting in an unsatisfactory estimation of the registration field (RF). In this study, we propose an end-to-end multispectral image registration (MSIR) network with unsupervised learning for human skin disease diagnosis. First, we propose a basic adjacent-band pair registration (ABPR) model to obtain the corresponding RFs through simultaneously modeling a series of image pairs from adjacent bands. Second, we introduce amultispectral attentionmodule (MAM) for extraction and adaptive weight allocation of the high-level pathological features of multiple MSI pairs. Third, we design a registration field refinement module (RFRM) to rectify and reconstruct a general RF solution. Fourth, we propose an unsupervised center-toward registration loss function, combining a similarity loss for features in the frequency domain and a smoothness loss forRF. In addition, we built aMSI dataset of multi-type skin diseases and conducted extensive experiments. The results show that our method not only outperforms state-of-the-art methods on MSI registration task, but also contributes to the subsequent task of benign and malignant disease classification.