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Diagnostic medical imaging plays an imperative role in clinical assessment and treatment of medical abnormalities. The fusion of multimodal medical images merges complementary information present in the multi‐source images and provides a better interpretation with improved diagnostic accuracy. This paper presents a CT–MR neurological image fusion method using an optimised biologically inspired neural network in nonsubsampled shearlet (NSST) domain. NSST decomposed coefficients are utilised to activate the optimised neural model using particle swarm optimisation method and to generate the firing maps. Low and high‐frequency NSST subbands get fused using max‐rule based on firing maps. In the optimisation process, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image. To analyse the fusion performance, extensive experiments are conducted on the different CT–MR neurological image dataset. Objective performance is evaluated based on different metrics to highlight the clarity, contrast, correlation, visual quality, complementary information, salient information, and edge information present in the fused images. Experimental results show that the proposed method is able to provide better‐fused images and outperforms other existing methods in both visual and quantitative assessments. [ABSTRACT FROM AUTHOR]

With the wide applications of biomedical images in the medical field, the segmentation of biomedical images plays an important role in clinical diagnosis, pathological analysis, and medical intervention. Full convolutional neural networks, especially U‐net, have improved the performance of segmentation greatly in recent years. However, due to their regular geometric structure, the standard convolutions that they use are inherently limited in dealing with geometric transformations while biomedical objects have huge variations in shape and size. In this study, the authors propose the DRU‐net, which is a novel U‐net with deformable encoder and reshaping upsampling convolution decoder, for biomedical image segmentation. First, deformable convolutional networks are applied and improved to enhance the learning ability of the encoder for geometric transformations. Second, a novel upsampling method named reshape upsampling convolution is proposed for better‐restoring resolution and fusion features. Furthermore, focal loss is used to address class imbalance and model overwhelmed problems in biomedical image segmentation tasks. Theoretic analysis and experimental results have shown that the proposed algorithm not only reduces the number of parameters of U‐Net, but also achieves produces competitive results compared with the state‐of‐the‐art algorithms in terms of various quantitative measures on Drosophila electron microscopy dataset and Warwick‐QU dataset. [ABSTRACT FROM AUTHOR]

Medical image quality requirements have been increasingly stringent with the recent developments of medical technology. To meet clinical diagnosis needs, an effective medical image enhancement method based on convolutional neural networks (CNNs) and frequency band broadening (FBB) is proposed. Curvelet transform is used to deal with medical data by obtaining the curvelet coefficient in each scale and direction, and the generalised cross‐validation is implemented to select the optimal threshold for performing denoising processing. Meanwhile, the cycle spinning scheme is used to wipe off the visible ringing effects along the edges of medical images. Then, FBB and a new CNN model based on the retinex model are used to improve the processed image resolution. Eventually, pixel‐level fusion is made between two enhanced medical images from CNN and FBB. In the authors' study, 50 groups of medical magnetic resonance imaging, X‐ray, and computed tomography images in total have been studied. The experimental results indicate that the final enhanced image using the proposed method outperforms other methods. The resolution and the edge details of the processed image are significantly enhanced, providing a more effective and accurate basis for medical workers to diagnose diseases. [ABSTRACT FROM AUTHOR]