In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured. 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). The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. 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. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. 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. Our algorithm was validated in dataset with modalities of several diseases, namely glioma, Alzheimer’s, and metastatic bronchogenic carcinoma, which contain more than 100 image pairs. Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.

The aim of infrared and visible image fusion is to obtain an integrated image that contains obvious object information and high spatial resolution background information. The integrated image is more conductive for a human or a machine to understand and mine the information contained in the image. To attain this purpose, a fusion algorithm based on multi-level Gaussian curvature filtering (MLGCF) image decomposition is proposed. First, a MLGCF is presented and employed to decompose the input source images into three different layers: small-scale, large-scale, and base layers. Then, three fusion strategies—max-value, integrated, and energy-based—are applied to combine the three types of layers, which are based on the different properties of the three types of layers. Finally, the fusion image is reconstructed by summing the three types of fused layers. Six groups of experiments demonstrate that the proposed algorithm performs effectively in most cases by subjective and objective evaluations and even exceeds many high-level fusion algorithms.

Hyperspectral anomaly detection is a challenging task due to the inaccurate evaluation of background statistics and the contamination of anomaly pixels. In this paper, we propose an effective hyperspectral anomaly detection algorithm based on local joint subspace process and support vector machine (SVM). The method mainly consists of three steps. At first, in the local joint subspace process, we combine Mahalanobis distance detection and spectral angular distance detection of subspace projection by local sliding change dual windows. With this local joint subspace process, the spatial-spectral information is jointly utilized and the local subspace score is also obtained. Then, to further improve the detection probability, we propose the support vector score by classifying the dimensionally reduced hyperspectral image data through a trained SVM model. Finally, we obtain the final detection result from the linear combination of above scores. Compared with alternative methods, the proposed method has shown a superior performance on both synthetic and real-world datasets.

Anomaly detection (AD) in hyperspectral target detection is of particular interest because no prior knowledge of ground object spectra is required. However, it is difficult to utilize the salient features of hyperspectral image (HSI) and mitigate the effects of noise in hyperspectral AD, which greatly limits the detection performance. Here, we report a strategy to implement hyperspectral AD by visual attention model and background subtraction with adaptive weight. Through band selection method, the most discriminating bands are selected as the input images for subsequent processing. Then, the hyperspectral visual attention model is introduced, for the first time, into hyperspectral AD for extracting the salient feature map of the input images. Furthermore, the background subtraction process that can reduce the background and noise in the salient feature map is developed via curvature filter. Using this operation, the initial anomaly area map is obtained. Finally, incorporating with the spectral information, an adaptive weight map is applied to the initial anomaly area map to further suppress the background. In the experiment, the proposed method is compared with seven other state-of-the-art methods on synthetic and real-world HSI. Most importantly, the results demonstrate that the proposed method is effective and performs better than alternative methods. We believe that this method can open a new avenue of visual processing methods for hyperspectral AD.

In this paper, a remote sensing image fusion method based on boundary measured dual-channel pulse-coupled neural network (PCNN) in multi-scale morphological gradient (MSMG) domain is proposed. Firstly, the panchromatic (PAN) image is decomposed into three parts, a small-scale image, a large-scale image, and a base image through a co-occurrence filtering (CoF)-based decomposition model. Secondly, an HSI transform is applied in the multispectral (MS) image to obtained intensity, hue and saturation components. Thirdly, a PCNN fusion strategy modulated by MSMG is used to fuse the base image and the intensity component of the MS image. Then, a fused intensity image is obtained by combining the small-scale image, large-scale image and the fused approximate image. Finally, the final fused image can be reconstructed by an inverse HSI transform. Experiments in four datasets demonstrate that the proposed method obtains the best performance in most cases.

The aim of multi-focus image fusion technology is to acquire an image of every scene all focused on the same visual point at different focal settings. To achieve this goal, we propose an improved multi-focus image fusion algorithm based on a Gaussian curvature filter and synthetic focusing degree criterion. First, in order to realize the salient feature extraction, a Gaussian curvature filter is applied to obtain the most sharpness regions. Then we obtain a coarse fusion map by composing a synthetic focusing degree criterion, which is a combination of the spatial frequency and the local variance of image. The coarse fusion map is further processed by morphological filters and median filters to acquire an optimized fusion map. Finally, the fusion image is obtained via a weighted fusion operation. Experimental results demonstrate that our proposed algorithm can be competitive with, or even outperform, many existing fusion methods on both qualitative and quantitative measures.