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.

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.

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.

A Medical Image Fusion Method Based on Contourlet Transformation and Region Sensitivity

2012 ◽  
Vol 195-196 ◽  
pp. 555-560
Author(s):  
Jun Li Li ◽  
Yi Na Qiu ◽  
Yang Lou
Keyword(s):  
Sensitivity Analysis ◽  
Image Fusion ◽  
Medical Image ◽  
Source Image ◽  
Fusion Method ◽  
Medical Image Fusion ◽  
Fused Image ◽  
And Performance ◽  
The Right ◽  
Image Fusion Method

The traditional wavelet-based image fusion method has some problems, in order to further satisfy the requirements of image fusion on the right direction, this paper studies the principle and performance contourlet transformation based on the proposed sensitivity analysis. In this method, contourlet multi-resolution, locality and direction effectively capture the source image in detail, texture, direction of information, enhance the visibility of the fused image and obtain the higher quality medical images.

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 Morphological Component Analysis-Based Perceptual Medical Image Fusion Using Convolutional Sparsity-Motivated PCNN

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chuangeng Tian ◽  
Lu Tang ◽  
Xiao Li ◽  
Kaili Liu ◽  
Jian Wang
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
State Of The Art ◽  
Component Analysis ◽  
Fusion Strategy ◽  
Medical Image Fusion ◽  
Morphological Component Analysis ◽  
Morphological Component ◽  
Fused Image ◽  
Fusion Framework

This paper proposes a perceptual medical image fusion framework based on morphological component analysis combining convolutional sparsity and pulse-coupled neural network, which is called MCA-CS-PCNN for short. Source images are first decomposed into cartoon components and texture components by morphological component analysis, and a convolutional sparse representation of cartoon layers and texture layers is produced by prelearned dictionaries. Then, convolutional sparsity is used as a stimulus to motivate the PCNN for dealing with cartoon layers and texture layers. Finally, the medical fused image is computed via combining fused cartoon layers and texture layers. Experimental results verify that the MCA-CS-PCNN model is superior to the state-of-the-art fusion strategy.

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.

scholarly journals An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

2017 ◽  
Vol 9 (4) ◽  
pp. 61 ◽  
Author(s):  
Guanqiu Qi ◽  
Jinchuan Wang ◽  
Qiong Zhang ◽  
Fancheng Zeng ◽  
Zhiqin Zhu
Keyword(s):  
Image Fusion ◽  
Dictionary Learning ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Fusion Framework

Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy

Oncology ◽  
2017 ◽  
pp. 519-541
Author(s):  
Satishkumar S. Chavan ◽  
Sanjay N. Talbar
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Frequency Domain ◽  
Medical Image ◽  
Treatment Plan ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion ◽  
Daubechies Complex Wavelet Transform ◽  
Fused Image ◽  
Complex Wavelet

The process of enriching the important details from various modality medical images by combining them into single image is called multimodality medical image fusion. It aids physicians in terms of better visualization, more accurate diagnosis and appropriate treatment plan for the cancer patient. The combined fused image is the result of merging of anatomical and physiological variations. It allows accurate localization of cancer tissues and more helpful for estimation of target volume for radiation. The details from both modalities (CT and MRI) are extracted in frequency domain by applying various transforms and combined them using variety of fusion rules to achieve the best quality of images. The performance and effectiveness of each transform on fusion results is evaluated subjectively as well as objectively. The fused images by algorithms in which feature extraction is achieved by M-Band Wavelet Transform and Daubechies Complex Wavelet Transform are superior over other frequency domain algorithms as per subjective and objective analysis.

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 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.

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