Medical Image Fusion Approach Research Articles

An Unsupervised Image Fusion Approach Using Convolution Neural Network for Feature Enhancement of Medical Brain Images

Image fusion is the process of amalgamation of two or more images into a single image that contains composite, enriched with diverse details from the original images. In the medical field, image fusion serves as an indispensable tool for elevating the precision of medical imagery and facilitating diagnostic processes. With the advent of deep learning, there has been a significant increase in the accuracy and effectiveness of image fusion techniques. This paper presents a deeplearningbased approach for medical image fusion that combines the advantages of deep-learning techniques with traditional image fusion methods. The proposed method is evaluated on medical data from different modalities, and the experimental results show that the proposed method outperforms existing state-of-the-art image fusion techniques.

Adaptive decomposition with guided filtering and Laplacian pyramid-based image fusion method for medical applications

Medical image fusion enhances diagnostic precision and facilitates clinical decision-making by integrating information from multiple medical imaging modalities. However, this field is still challenging as the output integrated image, whether from spatial or transform domain algorithms, may suffer from drawbacks such as low contrast, blurring effect, noise, over smoothness, etc. Also, some existing novel works are restricted to specific image datasets. So, to address such issues, a new multi-modal medical image fusion approach based on the advantageous effects of multiple transforms has been introduced in the present work. For this, we use an adaptive image decomposition tool known as Hilbert vibration decomposition (HVD). HVD decomposes an image into different energy components, and after a proper decomposition of the source images, the desirable features of the decomposed components are then passed through a guided filter (GF) for edge preservation. Then, the Laplacian pyramid integrates these filtered parts using the choose max rule. Since HVD offers better spatial resolution and is independent of fixed cut-off frequencies like other transforms, the subjective outputs from this method for different publicly available medical image datasets are clear and better than the previously 20 state-of-the-art published results. Moreover, the obtained values of different objective evaluation metrics such as information entropy (IE): 7.6943, 5.9737, mean: 110.6453, 54.6346, standard deviation (SD): 85.5376, 61.8129, average gradient (AG): 109.2818, 64.6451, spatial frequency (SF): 0.1475, 0.1100, and edge metric (QHK/S): 0.5400, 0.6511 demonstrate its comparability to others. The algorithm's running period of just 0.161244 s also indicates high computational efficiency.

Multi Modalities Medical Image Fusion Using Deep Learning and Metaverse Technology: Healthcare 4.0 A Futuristic Approach

Multimodal medical image fusion is the efficient integration of various imaging modalities to improve the ability to assess, direct therapy, treat patients, or predict outcomes. As image fusion offers additional essential information, the correctness of the image generated from different medical imaging modalities has a substantial impact on the success of a disease's diagnosis. A single medical imaging modality cannot provide complete and precise information. In the modern research area, multimodality medical image fusion approach is one of the meaningful research in the area of medical imaging and radiation medicine. The fusion of medical images is the process of enrolling and combining multiple images from one or more imaging modalities, enhancing the image quality and to achieve randomness and redundancy, heighten the clinical utility of medical images in the diagnosis and evaluation of medical problems. The thought is to enhance the image occurrence Magnetic resonance imaging (MRI) is achieved by combining images like computerized tomography (CT) as well as magnetic resonance imaging (MRI) gives fine soft tissue information as long as CT gives fine facts over denser tissue. In this research paper, we have an account that features for future development with demanding performance requirements and processing speed.

An Improved Multimodal Medical Image Fusion Approach Using Intuitionistic Fuzzy Set and Intuitionistic Fuzzy Cross-Correlation.

Multimodal medical image fusion (MMIF) is the process of merging different modalities of medical images into a single output image (fused image) with a significant quantity of information to improve clinical applicability. It enables a better diagnosis and makes the diagnostic process easier. In medical image fusion (MIF), an intuitionistic fuzzy set (IFS) plays a role in enhancing the quality of the image, which is useful for medical diagnosis. In this article, a new approach to intuitionistic fuzzy set-based MMIF has been proposed. Initially, the input medical images are fuzzified and then create intuitionistic fuzzy images (IFIs). Intuitionistic fuzzy entropy plays a major role in calculating the optimal value for three degrees, namely, membership, non-membership, and hesitation. After that, the IFIs are decomposed into small blocks and then perform the fusion rule. Finally, the enhanced fused image can be obtained by the defuzzification process. The proposed method is tested on various medical image datasets in terms of subjective and objective analysis. The proposed algorithm provides a better-quality fused image and is superior to other existing methods such as PCA, DWTPCA, contourlet transform (CONT), DWT with fuzzy logic, Sugeno's intuitionistic fuzzy set, Chaira's intuitionistic fuzzy set, and PC-NSCT. The assessment of the fused image is evaluated with various performance metrics such as average pixel intensity (API), standard deviation (SD), average gradient (AG), spatial frequency (SF), modified spatial frequency (MSF), cross-correlation (CC), mutual information (MI), and fusion symmetry (FS).

Medical image fusion based on multi-scale decomposition using hybrid deep learning network model

ABSTRACT In medical applications, effective medical diagnosis is developed by combining medical images from various morphologies using medical image fusion techniques. An accurate diagnosis cannot be made from a single modality image, and the existing approaches need better efficiency due to poor image quality and inconsistent performance. To solve this problem, this research proposes an effective artificial intelligence model based on multi-modal medical image fusion. This research develops a new method based on medical images using deep residual neural networks (ResNet-50) and a DarkNet-19. The optimised discrete wavelet transform (ODWT) is used for image decomposing. The original image’s high- and low-frequency coefficients are decomposed by using ODWT. The low-frequency coefficient is then fused using the Modified ResNet-50 model. After that, DarkNet-19 fuses the high-frequency coefficients, and the input stimulus for DarkNet-19 is a high-frequency coefficient’s modified average gradient. The inverse ODWT is employed to reconstruct the fused image finally. The proposed fusion model’s effectiveness is assessed using the various CT-MRI, CT-PET, and MRI-SPECT imaging datasets. The proposed medical image fusion approach attains a maximum performance for PSNR of 40.03 dB, Fusion Factor of 6.1025, Fusion Symmetry of 0.0869, and Visual Information Fidelity of 0.121.

Medical Image Fusion for Brain Tumor Detection

Medical image fusion is an important task in medical diagnosis that aims to provide a complete representation of medical images by combining multiple imaging modalities. The fused image is then fed into deep learning algorithms for tumor classification. In this paper, we propose an approach for medical image fusion of CT and MRI scan images for brain tumor detection. This work proposes an algorithm for the fusion of several imaging modalities, such as MRI and CT, based on a classifier with a fusion rule. Several qualitative and quantitative evaluation metrics have been used to assess the performance of the proposed method and compare it to cutting-edge image fusion techniques. On the basis of metrics like standard deviation, entropy, mutual information, etc., the experimental findings are assessed. In terms of accuracy and training loss metrics, the experimental results show that the proposed approach outperforms the individual modalities. As a result, the suggested technique can be employed as an effective and accurate instrument for the detection of brain cancers. The method can be used to increase diagnosis precision and decrease the false-negative rate, which will ultimately improve patient outcomes

A Novel Bacterial Foraging Optimization Based Multimodal Medical Image Fusion Approach

Multimodal medical image fusion (MIF) is the procedure of integrating different images in single into multiple imaging modalities for increasing the image quality by preserving a certain feature. Medical image combination covered a tremendous count of hot topic areas, involving pattern recognition, image processing, artificial intelligence (AI), computer vision (CV), and machine learning (ML). In addition, MIF was more commonly applied in clinical for physicians to understand the lesion by the combination of various modalities of medicinal image. This article introduces a novel bacterial foraging optimization-based multimodal medical image fusion approach (BFO-M3IFA). The presented BFO-M3IFA technique considered two distinct patterns of the images as the input of systems and the outcome will be the fused image. Primarily, the BFO-M3IFA technique exploits Weiner filtering (WF) technique as an image pre-processing step to get rid of the noise. Besides, discrete wavelet transform (DWT) was applied for decomposing the image into distinct subbands. Afterward, the estimated coefficients of modality 1 and comprehensive coefficients of modality 2 are integrated and vice versa. At last, a fusion rule is generated to fuse the details of two image modalities and the optimal fusion rule parameter is chosen with utilize of BFO algorithm. The experimental validation of the BFO-M3IFA system was tested and outcomes ensured the improved performance of the BFO-M3IFA system on existing models.

Medical image fusion using convolutional neural network

Medical image fusion methods combine medical pictures from many morphologies to improve the accuracy and reliability of medical diagnoses, and they are becoming more significant in a variety of clinical applications. This research introduces a convolutional neural network (CNN) based medical image fusion approach to create a fused picture with good visual quality and clear structural details. To generate a weight map, the proposed technique employs a trained Siamese convolutional network to fuse the pixel activity information of source pictures. Meanwhile, the original picture is decomposed using a contrast pyramid. Source pictures are combined using distinct spatial frequency bands and a weighted fusion operator. The suggested fusion method can successfully maintain the exact structural information of source pictures and generate excellent human visual effects, according to the findings of comparison trials.

DTCWTASODCNN: DTCWT based Weighted Fusion Model for Multimodal Medical Image Quality Improvement with ASO Technique & DCNN

Medical image fusion approaches are sub-categorized as single-mode as well as multimodal fusion strategies. The limitations of single-mode fusion approaches can be resolved by introducing a multimodal fusion approach. Multimodal medical image fusion approach is formed by integrating two or more medical images of similar or dissimilar modalities aims to enhance the image quality and to preserve the image information. Hence, this paper introduced a new way to meld multimodal medical images via utilizing developed weighted fusion model relied on Dual Tree Complex Wavelet Transform (DTCWT) for fusing the multimodal medical image. Here, the two medical images are considered for image fusion process and we have implied DTCWT to the medical images for generating four sub-bands partition of the source medical images. The Renyientropy-based weighted fusion model is used to combine the weighted coefficient of DTCWT of images. The final fusion process is carried out using Atom Search Sine Cosine Algorithm (ASSCA)-based Deep Convolutional Neural Network (DCNN). Moreover, the simulation work output demonstrated for developed fusion model gained the superior outcomes relied on key indicators named as Mutual Information i.e. MI, Peak Signal to Noise Ratio abbreviated as PSNR as well as Root Mean Square Error, in short RMSE with the values of 1.554, 40.45 dB as well as 5.554, correspondingly.

The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition

In medical image processing, magnetic resonance imaging (MRI) and computed tomography (CT) modalities are widely used to extract soft and hard tissue information, respectively. However, with the help of a single modality, it is very challenging to extract the required pathological features to identify suspicious tissue details. Several medical image fusion methods have attempted to combine complementary information from MRI and CT to address the issue mentioned earlier over the past few decades. However, existing methods have their advantages and drawbacks. In this work, we propose a new multimodal medical image fusion approach based on variational mode decomposition (VMD) and local energy maxima (LEM). With the help of VMD, we decompose source images into several intrinsic mode functions (IMFs) to effectively extract edge details by avoiding boundary distortions. LEM is employed to carefully combine the IMFs based on the local information, which plays a crucial role in the fused image quality by preserving the appropriate spatial information. The proposed method’s performance is evaluated using various subjective and objective measures. The experimental analysis shows that the proposed method gives promising results compared to other existing and well-received fusion methods.

An improved approach for medical image fusion using sparse representation and Siamese convolutional neural network

Multimodal image fusion is a contemporary branch of medical imaging that aims to increase the accuracy of clinical diagnosis of the disease stage development. The fusion of different image modalities can be a viable medical imaging approach. It combines the best features to produce a composite image with higher quality than its predecessors and can significantly improve medical diagnosis. Recently, sparse representation (SR) and Siamese Convolutional Neural Network (SCNN) methods have been introduced independently for image fusion. However, some of the results from these approaches have recorded defects, such as edge blur, less visibility, and blocking artifacts. To remedy these deficiencies, in this paper, a smart blending approach based on a combination of SR and SCNN is introduced for image fusion, which comprises three steps as follows. Firstly, entire source images are fed into the classical orthogonal matching pursuit (OMP), where the SR-fused image is obtained using the max-rule that aims to improve pixel localization. Secondly, a novel scheme of SCNN-based K-SVD dictionary learning is re-employed for each source image. The method has shown good non-linearity behavior, contributing to increasing the fused output's sparsity characteristics and demonstrating better extraction and transfer of image details to the output fused image. Lastly, the fusion rule step employs a linear combination between steps 1 and 2 to obtain the final fused image. The results depict that the proposed method is advantageous, compared to other previous methods, notably by suppressing the artifacts produced by the traditional SR and SCNN model.

OPTIMIZED DUAL-TREE COMPLEX WAVELET TRANSFORM AND FUZZY ENTROPY FOR MULTI-MODAL MEDICAL IMAGE FUSION: A HYBRID META-HEURISTIC CONCEPT

In recent times, multi-modal medical image fusion has emerged as an important medical application tool. An important goal is to fuse the multi-modal medical images from diverse imaging modalities into a single fused image. The physicians broadly utilize this for precise identification and treatment of diseases. This medical image fusion approach will help the physician perform the combined diagnosis, interventional treatment, pre-operative planning, and intra-operative guidance in various medical applications by developing the corresponding information from clinical images through different modalities. In this paper, a novel multi-modal medical image fusion method is adopted using the intelligent method. Initially, the images from two different modalities are applied with optimized Dual-Tree Complex Wavelet Transform (DT-CWT) for splitting the images into high-frequency subbands and low-frequency subbands. As an improvement to the conventional DT-CWT, the filter coefficients are optimized by the hybrid meta-heuristic algorithm named as Hybrid Beetle and Salp Swarm Optimization (HBSSO) by merging the Salp Swarm Algorithm (SSA), and Beetle Swarm Optimization (BSO). Moreover, the fusion of the source images’ high-frequency subbands was done by the optimized type-2 Fuzzy Entropy. The upper and lower membership limits are optimized by the same hybrid HBSSO. The optimized type-2 fuzzy Entropy automatically selects high-frequency coefficients. Also, the fusion of the low-frequency sub-images is performed by the Averaging approach. Further, the inverse optimized DT-CWT on the fused image sets helps to obtain the final fused medical image. The main objective of the optimized DT-CWT and optimized type-2 fuzzy Entropy is to maximize the SSIM. The experimental results confirm that the developed approach outperforms the existing fusion algorithms in diverse performance measures.

Medical image fusion using segment graph filter and sparse representation

This study proposes a novel medical image fusion approach based on the segment graph filter (SGF) and sparse representation (SR). Specifically, using the SGF, source images are decomposed into base and detail images, based on which the edge information is integrated into the fused image as much as possible. The base images are then fused applying a fusion rule based on the normalized Shannon entropy, whereas the detail images are fused using an SR-based fusion method. Finally, the resultant fused image is computed by combining the fused base and detail images. For quantitative performance evaluations, five metrics are adopted: the feature-based metric, structure-based metric, normalized mutual information, nonlinear correlation information entropy, and phase congruency metric. Experimental results indicate that the fusion performance of the proposed method is comparable to those of state-of-the-art methods with respect to both subjective visual performance and objective quantification.

Multimodal medical image fusion using empirical wavelet decomposition and local energy maxima

In the clinical environment, medical imaging plays a prominent role in assisting radiologists/doctors. The information present in the images is crucial especially during the time of diagnosis. This can be considerably improved with the help of multimodal medical image fusion approach, by integrating the information from various imaging modalities. In recent years, various methodologies are developed to fuse medical images. However, the multimodal medical image fusion is still a challenging issue, due to the degradation of medical images at the acquisition phase. To address this problem, we combine the complementary information from the various distinct imaging modalities such as MRI, PET, and SPECT by reducing the distortion using empirical wavelet transform (EWT) representation and local energy maxima (LEM) fusion rule. In EWT, the basis functions are selected optimally, depending on the nature of the input image. It results in preserving critical information like edges which are crucial in image fusion. The use of LEM highlights, vital information with the help of local energy constraint. We compared the proposed methodology with the state -of- the -art approaches and observed improved performance in terms of visual quality as well as fusion metrics.

A Novel Medical Image Fusion Approach Based on Nonsubsampled Shearlet Transform

A Novel Medical Image Fusion Approach Based on Nonsubsampled Shearlet Transform

An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching

This paper introduces an optimal solution for wavelet-based medical image fusion using different wavelet families and Principal Component Ana1ysis (PCA) based on the Modified Central Force Optimization (MCFO) technique. The main motivation of this work is to increase the quality of medical fused images in order to provide correct diagnosis of diseases for the objective of optimal therapy. This can be achieved by fusing medical images of different modalities using an optimization technique based on the MCFO. The MCFO technique gives the optimum gain parameters that achieve the best fused image quality. Histogram matching is applied to improve the overall values of the Peak Signal-to-Noise Ratio (PSNR), entropy, local contrast, and quality of the fused image. A comparative study is performed between the proposed algorithm, the traditional Discrete Wavelet Transform (DWT), and the PCA fusion using maximum fusion rule. The proposed algorithm is evaluated subjectively and objectively with different fusion quality metrics. Simulation results demonstrate that the proposed MCFO optimized wavelet-based fusion algorithm using Haar wavelet and histogram matching achieves a superior performance with the highest image quality and clearest image details in a very short processing time.

Optimal multi‐scale geometric fusion based on non‐subsampled contourlet transform and modified central force optimization

AbstractIn the current era of technological development, medical imaging plays an important part in several applications of medical diagnosis and therapy. This requires more precise images with much more details and information for correct medical diagnosis and therapy. Medical image fusion is one of the solutions for obtaining much spatial and spectral information in a single image. This article presents an optimization‐based contourlet image fusion approach in addition to a comparative study for the performance of both multi‐resolution and multi‐scale geometric effects on fusion quality. An optimized multi‐scale fusion technique based on the Non‐Subsampled Contourlet Transform (NSCT) using the Modified Central Force Optimization (MCFO) and local contrast enhancement techniques is presented. The first step in the proposed fusion approach is the histogram matching of one of the images to the other to allow the same dynamic range for both images. The NSCT is used after that to decompose the images to be fused into their coefficients. The MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. The proposed fusion framework is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation (STD), edge intensity, entropy, peak signal‐to‐noise ratio, Q ab/f, and processing time. Experimental results demonstrate that the proposed optimized NSCT medical image fusion approach based on the MCFO and histogram matching achieves a superior performance with higher image quality, average gradient, edge intensity, STD, better local contrast and entropy, a good quality factor, and much more details in images. These characteristics help for more accurate medical diagnosis in different medical applications.

An Enhanced Approach for Medical Image Fusion Using Hybrid of GWO and State Transition

An Enhanced Approach for Medical Image Fusion Using Hybrid of GWO and State Transition

A hybrid approach for Medical Image Fusion Based on Wavelet Transform and Principal Component Analysis

This paper presents a hybrid approach for medical image fusion based on the Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). The main idea of the approach is to select between two fusion methods; DWT and PCA based on the local variance estimated at each position in the fusion results. Simulation results on multi-modality images are presented in this paper. The two modalities adopted are Magnetic Resonance (MR) images and Computed Tomography (CT) images. Evaluation metrics such as entropy, edge intensity, contrast, and average gradient have been adopted for performance evaluation of the proposed method. The obtained results confirm that the proposed method is superior in performance to the DWT and PCA methods individually.

A Novel Multimodal Medical Image Fusion Approach based on Phase Congruency and Directive Contrast in NSCT Domain

In this paper, Non-subsampled medical image fusion is a unique tool, which develops many imaging techniques in medical field. The main work is to capture the information from different image sources and convert them into single output. Two different fusion rules like phase congruency and directive contrast are introduced. The work was discussed based on the images in medical field which gives accurate results with less distortion is based upon transformation and parameters. The main drawback of previous methods are they cannot produce a color image for better clarity and accurate analysis of medical image. In this paper, the parameters such as Mutual Information(MI), Edge Based Similarity Measure(QAB/F), Structural Information Metric(Qe), Degree Of Distortion(Qo) and Normalization Of Image(Qw) are introduced to increase the visual perception of an image. The parameters and lab-transform is used for better quality of the image.