Multi-modality image fusion provides more comprehensive and sophisticated information in modern medical diagnosis, remote sensing, video surveillance, and so on. This paper presents a novel multi-modality medical image fusion method based on phase congruency and local Laplacian energy. In the proposed method, the non-subsampled contourlet transform is performed on medical image pairs to decompose the source images into high-pass and low-pass subbands. The high-pass subbands are integrated by a phase congruency-based fusion rule that can enhance the detailed features of the fused image for medical diagnosis. A local Laplacian energy-based fusion rule is proposed for low-pass subbands. The local Laplacian energy consists of weighted local energy and the weighted sum of Laplacian coefficients that describe the structured information and the detailed features of source image pairs, respectively. Thus, the proposed fusion rule can simultaneously integrate two key components for the fusion of low-pass subbands. The fused high-pass and low-pass subbands are inversely transformed to obtain the fused image. In the comparative experiments, three categories of multi-modality medical image pairs are used to verify the effectiveness of the proposed method. The experiment results show that the proposed method achieves competitive performance in both the image quantity and computational costs.

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A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain

Smart health care is an important aspect of connected living. Health care is one of the basic pillars of human need, and smart health care is projected to produce several billion dollars in revenue in the near future. There are several components of smart health care, including the Internet of Things (IoT), the Internet of Medical Things (IoMT), medical sensors, artificial intelligence (AI), edge computing, cloud computing, and next-generation wireless communication technology. Many papers in the literature deal with smart health care or health care in general. Here, we present a comprehensive survey of IoT- and IoMT-based edge-intelligent smart health care, mainly focusing on journal articles published between 2014 and 2020. We survey this literature by answering several research areas on IoT and IoMT, AI, edge and cloud computing, security, and medical signals fusion. We also address current research challenges and offer some future research directions.

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A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion

Medical image fusion aims at integrating information from multimodality medical images to obtain a more complete and accurate description of the same object, which provides an easy access for image-guided medical diagnostic and treatment Unfortunately, medical images are often corrupted by noise in acquisition or transmission, and the noise signal is easily mistaken for a useful characterization of the image, making the fusion effect drop significantly Thus, the existence of noise presents a great challenge for most of traditional image fusion methods To address this problem, an effective variation model for multimodality medical image fusion and denoising is proposed First, a multiscale alternating sequential filter is exploited to extract the useful characterizations (eg, details and edges) from noisy input medical images Then, a recursive filtering-based weight map is constructed to guide the fusion of main features of input images Additionally, total variation (TV) constraint is developed by constructing an adaptive fractional order $p$ based on the local contrast of fused image, further effectively suppressing noise while avoiding the staircase effect of the TV The experimental results indicate that the proposed method performs well with both noisy and normal medical images, outperforming conventional methods in terms of fusion quality and noise reduction

Medical Image Fusion and Denoising with Alternating Sequential Filter and Adaptive Fractional Order Total Variation

Optimum spectrum mask based medical image fusion using Gray Wolf Optimization

An improved multimodal medical image fusion algorithm based on fuzzy transform

A novel method of multimodal medical image fusion using fuzzy transform

Medical image fusion produces a fused image that is extensively used by physicians for medical analysis and treatment. The fused image, so obtained, contains the complementary features present in different medical images obtained from imaging devices of single modality or of multiple modalities. The potential capabilities of waveatoms have been explored in many applications such as image denoising, fingerprint identification, compression; therefore, waveatom transform-based medical image fusion is proposed. The proposed fusion method is experimented on various sets of medical images and compared with recent state-of-the-art fusion methods. Results prove that the fused images obtained from the proposed method have better clarity and enhanced information and are practically more helpful for quick diagnosis and better treatment of diseases.

Waveatom transform-based multimodal medical image fusion

Medical image fusion is being used at large by clinical professionals for improved diagnosis and treatment of diseases. The main aim of image fusion process is to combine complete information from all input images into a single fused image. Therefore, a novel fusion rule is proposed for fusing medical images based on Daubechies complex wavelet transform (DCxWT). Input images are first decomposed using DCxWT. The complex coefficients so obtained are then fused using normalized correlation based fusion rule. Finally, the fused image is obtained by inverse DCxWT with all combined complex coefficients. The performance of the proposed method has been evaluated and compared both visually and objectively with DCxWT based fusion methods using state-of art fusion rules as well as with existing fusion techniques. Experimental results and comparative study demonstrate that the proposed fusion technique generates better results than existing fusion rules as well as with other fusion techniques.

Meenu Manchanda

Papers

An improved multimodal medical image fusion algorithm based on fuzzy transform

A novel method of multimodal medical image fusion using fuzzy transform

Waveatom transform-based multimodal medical image fusion

A Novel Fusion Rule for Medical Image Fusion in Complex Wavelet Transform Domain

Fusion of Fuzzy Enhanced Overexposed and Underexposed Images