Medical Image Fusion in Wavelet and Ridgelet Domains

2017 ◽  
pp. 711-723
Author(s):  
Vikrant Bhateja ◽  
Abhinav Krishn ◽  
Himanshi Patel ◽  
Akanksha Sahu
Keyword(s):  
Image Fusion ◽  
Information Content ◽  
Medical Image ◽  
Similarity Index ◽  
Principal Component ◽  
Structural Similarity ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Multi Scale ◽  
Fused Image

Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of life threatening diseases. Fusion has been performed using various approaches such as Pyramidal, Multi-resolution, multi-scale etc. Each and every approach of fusion depicts only a particular feature (i.e. the information content or the structural properties of an image). Therefore, this paper presents a comparative analysis and evaluation of multi-modal medical image fusion methodologies employing wavelet as a multi-resolution approach and ridgelet as a multi-scale approach. The current work tends to highlight upon the utility of these approaches according to the requirement of features in the fused image. Principal Component Analysis (PCA) based fusion algorithm has been employed in both ridgelet and wavelet domains for purpose of minimisation of redundancies. Simulations have been performed for different sets of MR and CT-scan images taken from ‘The Whole Brain Atlas'. The performance evaluation has been carried out using different parameters of image quality evaluation like: Entropy (E), Fusion Factor (FF), Structural Similarity Index (SSIM) and Edge Strength (QFAB). The outcome of this analysis highlights the trade-off between the retrieval of information content and the morphological details in finally fused image in wavelet and ridgelet domains.

Medical Image Fusion in Wavelet and Ridgelet Domains

2015 ◽  
Vol 2 (2) ◽  
pp. 78-91 ◽  
Author(s):  
Vikrant Bhateja ◽  
Abhinav Krishn ◽  
Himanshi Patel ◽  
Akanksha Sahu
Keyword(s):  
Image Fusion ◽  
Information Content ◽  
Medical Image ◽  
Similarity Index ◽  
Principal Component ◽  
Brain Atlas ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Multi Scale ◽  
Fused Image

Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of life threatening diseases. Fusion has been performed using various approaches such as Pyramidal, Multi-resolution, multi-scale etc. Each and every approach of fusion depicts only a particular feature (i.e. the information content or the structural properties of an image). Therefore, this paper presents a comparative analysis and evaluation of multi-modal medical image fusion methodologies employing wavelet as a multi-resolution approach and ridgelet as a multi-scale approach. The current work tends to highlight upon the utility of these approaches according to the requirement of features in the fused image. Principal Component Analysis (PCA) based fusion algorithm has been employed in both ridgelet and wavelet domains for purpose of minimisation of redundancies. Simulations have been performed for different sets of MR and CT-scan images taken from ‘The Whole Brain Atlas'. The performance evaluation has been carried out using different parameters of image quality evaluation like: Entropy (E), Fusion Factor (FF), Structural Similarity Index (SSIM) and Edge Strength (QFAB). The outcome of this analysis highlights the trade-off between the retrieval of information content and the morphological details in finally fused image in wavelet and ridgelet domains.

scholarly journals Multimodal Medical Image Fusion using Guided Filter in NSCT Domain

2018 ◽  
Vol 11 (4) ◽  
pp. 1937-1946
Author(s):  
Nancy Mehta ◽  
Sumit Budhiraja
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Similarity Index ◽  
Low Frequency ◽  
Structural Similarity ◽  
Relevant Information ◽  
Phase Congruency ◽  
Edge Preserving ◽  
Medical Image Fusion ◽  
Fused Image

Multimodal medical image fusion aims at minimizing the redundancy and collecting the relevant information using the input images acquired from different medical sensors. The main goal is to produce a single fused image having more information and has higher efficiency for medical applications. In this paper modified fusion method has been proposed in which NSCT decomposition is used to decompose the wavelet coefficients obtained after wavelet decomposition. NSCT being multidirectional,shift invariant transform provide better results.Guided filter has been used for the fusion of high frequency coefficients on account of its edge preserving property. Phase congruency is used for the fusion of low frequency coefficients due to its insensitivity to illumination contrast hence making it suitable for medical images. The simulated results show that the proposed technique shows better performance in terms of entropy, structural similarity index, Piella metric. The fusion response of the proposed technique is also compared with other fusion approaches; proving the effectiveness of the obtained fusion results.

scholarly journals Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

2018 ◽  
Vol 7 (2.31) ◽  
pp. 165
Author(s):  
M Shyamala Devi ◽  
P Balamurugan
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Gabor Filter ◽  
Low Frequency ◽  
Local Energy ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Image Averaging

Image processing technology requires moreover the full image or the part of image which is to be processed from the user’s point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edge-like features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work .

scholarly journals Multi-Modal Medical Image Fusion Based on FusionNet in YIQ Color Space

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1423
Author(s):  
Kai Guo ◽  
Xiongfei Li ◽  
Hongrui Zang ◽  
Tiehu Fan
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Medical Information ◽  
Color Space ◽  
Structural Similarity ◽  
Source Image ◽  
Medical Image Fusion ◽  
Structure Recognition ◽  
Fused Image ◽  
Fusion Framework

In order to obtain the physiological information and key features of source images to the maximum extent, improve the visual effect and clarity of the fused image, and reduce the computation, a multi-modal medical image fusion framework based on feature reuse is proposed. The framework consists of intuitive fuzzy processing (IFP), capture image details network (CIDN), fusion, and decoding. First, the membership function of the image is redefined to remove redundant features and obtain the image with complete features. Then, inspired by DenseNet, we proposed a new encoder to capture all the medical information features in the source image. In the fusion layer, we calculate the weight of each feature graph in the required fusion coefficient according to the trajectory of the feature graph. Finally, the filtered medical information is spliced and decoded to reproduce the required fusion image. In the encoding and image reconstruction networks, the mixed loss function of cross entropy and structural similarity is adopted to greatly reduce the information loss in image fusion. To assess performance, we conducted three sets of experiments on medical images of different grayscales and colors. Experimental results show that the proposed algorithm has advantages not only in detail and structure recognition but also in visual features and time complexity compared with other algorithms.

Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis

2019 ◽  
pp. 206-233 ◽  
Author(s):  
Rajalingam B. ◽  
Priya R. ◽  
Bhavani R.
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Medical Image ◽  
Single Photon ◽  
Photon Emission ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Visual Description ◽  
The Individual

In this chapter, different types of image fusion techniques have been studied and evaluated in the medical applications. The ultimate goal of this proposed method is to obtain the fused image without any loss of similar information and preserve all special features present in the input medical images. This method is used to improve the fused image quality for better diagnosis of critical disease analysis. The fused hybrid multimodal medical image should convey better visual description than the individual input images. This chapter proposes the method for multimodal medical image fusion using the hybrid fusion algorithm. The computed tomography, magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input images used for this experimental work. In this chapter, experimental results discovered that the proposed techniques provide better visualization of fused image and gives the superior results compared to various existing traditional algorithms.

Medical image fusion based on modified pulse coupled neural network model and kirsch operator

2019 ◽  
Vol 17 (06) ◽  
pp. 1950052 ◽  
Author(s):  
Guofen Wang ◽  
Yongdong Huang
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Feature Maps ◽  
Fusion Algorithm ◽  
Significant Information ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Weight Coefficients

The medical image fusion process integrates the information of multiple source images into a single image. This fused image can provide more comprehensive information and is helpful in clinical diagnosis and treatment. In this paper, a new medical image fusion algorithm is proposed. Firstly, the original image is decomposed into a low-frequency sub-band and a series of high-frequency sub-bands by using nonsubsampled shearlet transform (NSST). For the low-frequency sub-band, kirsch operator is used to extract the directional feature maps from eight directions and novel sum-modified-Laplacian (NSML) method is used to calculate the significant information of each directional feature map, and then, combining a sigmod function and the significant information updated by gradient domain guided image filtering (GDGF), calculate the fusion weight coefficients of the directional feature maps. The fused feature map is obtained by summing the convolutions of the weight coefficients and the directional feature maps. The final fused low-frequency sub-band is obtained by the linear combination of the eight fused directional feature maps. The modified pulse coupled neural network (MPCNN) model is used to calculate the firing times of each high-frequency sub-band coefficient, and the fused high-frequency sub-bands are selected according to the firing times. Finally, the inverse NSST acts on the fused low-frequency sub-band and the fused high-frequency sub-bands to obtain the fused image. The experimental results show that the proposed medical image fusion algorithm expresses some advantages over the classical medical image fusion algorithms in objective and subjective evaluation.

scholarly journals Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xi-Cheng Lou ◽  
Xin Feng
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Hadamard Product ◽  
Subjective Evaluation ◽  
Low Rank ◽  
Source Image ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Low Rank Representation

A multimodal medical image fusion algorithm based on multiple latent low-rank representation is proposed to improve imaging quality by solving fuzzy details and enhancing the display of lesions. Firstly, the proposed method decomposes the source image repeatedly using latent low-rank representation to obtain several saliency parts and one low-rank part. Secondly, the VGG-19 network identifies the low-rank part’s features and generates the weight maps. Then, the fused low-rank part can be obtained by making the Hadamard product of the weight maps and the source images. Thirdly, the fused saliency parts can be obtained by selecting the max value. Finally, the fused saliency parts and low-rank part are superimposed to obtain the fused image. Experimental results show that the proposed method is superior to the traditional multimodal medical image fusion algorithms in the subjective evaluation and objective indexes.

scholarly journals Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2169 ◽  
Author(s):  
Kunpeng Wang ◽  
Mingyao Zheng ◽  
Hongyan Wei ◽  
Guanqiu Qi ◽  
Yuanyuan Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Medical Image ◽  
Source Image ◽  
Fusion Algorithm ◽  
Convolutional Network ◽  
Structure Information ◽  
Medical Image Fusion ◽  
Fused Image

Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high visual quality and clear structure details, this paper proposes a convolutional neural network (CNN) based medical image fusion algorithm. The proposed algorithm uses the trained Siamese convolutional network to fuse the pixel activity information of source images to realize the generation of weight map. Meanwhile, a contrast pyramid is implemented to decompose the source image. According to different spatial frequency bands and a weighted fusion operator, source images are integrated. The results of comparative experiments show that the proposed fusion algorithm can effectively preserve the detailed structure information of source images and achieve good human visual effects.

Multimodal Medical Image Fusion Using Rolling Guidance Filter with CNN and Nuclear Norm Minimization

2020 ◽  
Vol 16 ◽  
Author(s):  
Shuaiqi Liu ◽  
Lu Yin ◽  
Siyu Miao ◽  
Jian Ma ◽  
Shuai Cong ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Nuclear Norm ◽  
Base Layer ◽  
Feature Mapping ◽  
Fusion Algorithm ◽  
Nuclear Norm Minimization ◽  
Medical Image Fusion ◽  
Norm Minimization ◽  
Fused Image

Background: Medical image fusion is very important for diagnosis and treatment of disease. In recent years, there are lots of different multimodal medical image fusion algorithms which can provide delicate contexts for disease diagnosis more clearly and more convenient. Recently, nuclear norm minimization and deep learning have been used effectively in image processing. Method: A multi-modality medical image fusion method using rolling guidance filter (RGF) with convolutional neural network (CNN) based feature mapping and nuclear norm minimization (NNM) is proposed. At first, we decompose medical images to base layer components and detail layer components by using RGF. In the next step, we get the basic fused image through the pre-trained CNN model. The CNN model with pre-training is used to obtain the significant characteristics of the base layer components. And we can compute the activity level measurement from the regional energy of CNN-based fusion maps. Then, a detail fused image is gained by NNM. That is, we use NNM to fuse the detail layer components. At last, the basic and detail fused images are integrated into the fused result. Results: From the comparison with the most advanced fusion algorithms, the results of experiments indicate that this fusion algorithm has the best effect in visual evaluation and objective standard. Conclusion: The fusion algorithm using RGF and CNN-based feature mapping combined with NNM can improve fusion effects and suppress artifacts and blocking effects in the fused result.

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