A Comparative Analysis of Multimodality Medical Image Fusion Methods

2012 ◽  
Author(s):  
Kiran Parmar ◽  
Rahul Kher
Keyword(s):  
Comparative Analysis ◽  
Image Fusion ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Fusion Methods

scholarly journals A Review of Multimodal Medical Image Fusion Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Bing Huang ◽  
Feng Yang ◽  
Mengxiao Yin ◽  
Xiaoying Mo ◽  
Cheng Zhong
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Development Trend ◽  
Research Field ◽  
Imaging Modalities ◽  
Data Set ◽  
Medical Image Fusion ◽  
Multiple Imaging ◽  
Fused Image ◽  
Fusion Methods

The medical image fusion is the process of coalescing multiple images from multiple imaging modalities to obtain a fused image with a large amount of information for increasing the clinical applicability of medical images. In this paper, we attempt to give an overview of multimodal medical image fusion methods, putting emphasis on the most recent advances in the domain based on (1) the current fusion methods, including based on deep learning, (2) imaging modalities of medical image fusion, and (3) performance analysis of medical image fusion on mainly data set. Finally, the conclusion of this paper is that the current multimodal medical image fusion research results are more significant and the development trend is on the rise but with many challenges in the research field.

scholarly journals Medical image fusion based on variational and nonlinear structure tensor

2018 ◽  
Vol 189 ◽  
pp. 10021
Author(s):  
Xiaobei Wang ◽  
Rencan Nie ◽  
Xiaopeng Guo
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Energy Functional ◽  
Structure Tensor ◽  
Fusion Method ◽  
Edge Information ◽  
Medical Image Fusion ◽  
Fused Image ◽  
New Energy ◽  
Fusion Methods

Medical image fusion plays an important role in detection and treatment of disease. Although numerous medical image fusion methods have been proposed, most of them decrease the contrast and lose the image information. In this paper, a novel MRI and CT image fusion method is proposed combining rolling guidance filter, structure tensor, and nonsubsampled shearlet transform (NSST). First, the rolling guidance filter and the sum-modified laplacian (SML) operator are introduced in the algorithm to construct the weight maps in non-linear domain, then the fused gradient is firstly obtained by a new weighted structure tensor fusion method, and the fused image is firstly acquired in NSST domain, finally, a new energy functional is defined to constrain the gradient and pixel information of the final fused image close to the pre-fused gradient and the pre-fused image, experimental results show that the proposed method can retain the edge information of source images effectively and preserve the reduction of contrast.

scholarly journals Multimodal Medical Image Fusion by Adaptive Manifold Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Peng Geng ◽  
Shuaiqi Liu ◽  
Shanna Zhuang
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Low Frequency ◽  
Filter Method ◽  
Local Contrast ◽  
Complex Wavelet Transform ◽  
Image Guided Radiotherapy ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Fusion Methods

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.

scholarly journals A Comprehensive Review of Multimodal Medical Image Fusion Techniques

2020 ◽  
pp. 407-410
Author(s):  
Jakir Hussain G K ◽  
Tamilanban R ◽  
Tamilselvan K S ◽  
Vinoth Saravanan M
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Pedestrian Detection ◽  
Relevant Information ◽  
Medical Diagnostics ◽  
Optimization Techniques ◽  
Medical Image Fusion ◽  
Multiple Imaging ◽  
Fused Image ◽  
Fusion Methods

The multimodal image fusion is the process of combining relevant information from multiple imaging modalities. A fused image which contains recovering description than the one provided by any image fusion techniques are most widely used for real-world applications like agriculture, robotics and informatics, aeronautical, military, medical, pedestrian detection, etc. We try to give an outline of multimodal medical image fusion methods, developed during the period of time. The fusion of medical images in many combinations assists in utilizing it for medical diagnostics and examination. There is an incredible progress within the fields of deep learning, AI and bio-inspired optimization techniques. Effective utilization of these techniques is often used to further improve the effectiveness of image fusion algorithms.

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

2021 ◽  
Vol 11 (22) ◽  
pp. 10975
Author(s):  
Srinivasu Polinati ◽  
Durga Prasad Bavirisetti ◽  
Kandala N V P S Rajesh ◽  
Ganesh R Naik ◽  
Ravindra Dhuli
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Spatial Information ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Medical Image Fusion ◽  
Mode Decomposition ◽  
Fused Image ◽  
Fusion Methods ◽  
Magnetic Resonance Imaging Mri

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.

Image Fusion Techniques for Different Multimodality Medical Images Based on Various Conventional and Hybrid Algorithms for Disease Analysis

2020 ◽  
pp. 159-196
Author(s):  
Rajalingam B. ◽  
Priya R. ◽  
Bhavani R. ◽  
Santhoshkumar R.
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Medical Images ◽  
Experimental Result ◽  
Accurate Information ◽  
Limited Information ◽  
Healthcare Applications ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Fusion Methods

Image fusion is the process of combining two or more images to form a single fused image, which can provide more reliable and accurate information. Over the last few decades, medical imaging plays an important role in a large number of healthcare applications including diagnosis, treatment, etc. The different modalities of medical images contain complementary information of human organs and tissues, which help the physicians to diagnose the diseases. The multimodality medical images can provide limited information. These multimodality medical images cannot provide comprehensive and accurate information. This chapter proposed and examines some of the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods. The hybrid multimodal medical image fusion algorithms are used to improve the quality of fused multimodality medical image. An experimental result of proposed hybrid fusion techniques provides the fused multimodal medical images of highest quality, shortest processing time, and best visualization.

scholarly journals Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Liu Shuaiqi ◽  
Zhao Jie ◽  
Shi Mingzhu
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Medical Images ◽  
Cortical Model ◽  
Image Guided Radiotherapy ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Fusion Methods ◽  
Mean And Variance ◽  
The Mean

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF) and spiking cortical model (SCM). Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.

scholarly journals Medical Image Fusion Based on Low-Level Features

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yongxin Zhang ◽  
Chenrui Guo ◽  
Peng Zhao
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Saliency Detection ◽  
Medical Problems ◽  
Low Level ◽  
Medical Image Fusion ◽  
Spatial Consistency ◽  
Fused Image ◽  
Fusion Methods ◽  
Clinical Accuracy

Medical image fusion is an important technique to address the limited depth of the optical lens for a completely informative focused image. It can well improve the accuracy of diagnosis and assessment of medical problems. However, the difficulty of many traditional fusion methods in preserving all the significant features of the source images compromises the clinical accuracy of medical problems. Thus, we propose a novel medical image fusion method with a low-level feature to deal with the problem. We decompose the source images into base layers and detail layers with local binary pattern operators for obtaining low-level features. The low-level features of the base and detail layers are applied to construct weight maps by using saliency detection. The weight map optimized by fast guided filtering guides the fusion of base and detail layers to maintain the spatial consistency between the source images and their corresponding layers. The recombination of the fused base and detail layers constructs the final fused image. The experimental results demonstrated that the proposed method achieved a state-of-the-art performance for multifocus images.

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