It is based on the application of a boundary measured pulse-coupled neural network fusion strategy and an energy attribute fusion strategy in a non-subsampled shearlet transform domain. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. Each of these images will represent a modality that will render the examined organ differently, leading to different observations of a given phenomenon (such as stroke). In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured.
