An Improved Medical Image Fusion Method Based on Nonsubsampled Contourlet Transform

2014 ◽  
Vol 989-994 ◽  
pp. 1082-1087
Author(s):  
Yan Chun Yang ◽  
Jian Wu Dang ◽  
Yang Ping Wang
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Low Frequency ◽  
Fusion Rule ◽  
Nonsubsampled Contourlet Transform ◽  
Rule Based ◽  
Medical Image Fusion ◽  
Single Pixel ◽  
Image Fusion Method

In order to further improve the quality of medical image fusion,an improved medical image fusion method, based on nonsubsampled contourlet transform (NSCT),is proposed in the paper. A fusion rule based on the improved pulse coupled neural network (PCNN) is adopted in low frequency sub-band coefficient. Because human visual is more sensitive to all local region pixels instead of single pixel,it is more reasonable that the region information stimulates PCNN instead of single pixel. Each neuron of PCNN model is stimulated by the region spatial frequency of low frequency sub-band coefficient .Low frequency sub-band coefficient is determined by the times of firing. When choosing the bandpass directional sub-band coefficients, the directional characteristics of NSCT has been made best use of in the paper.A fusion rule based on sum-modified Laplacian is presented in bandpass directional sub-band cosfficients.The experiment results show that the proposed method can greatly improve the quality of fusion image compared with traditional fusion methods.

A Nonsubsampled Contourlet Transform Based Medical Image Fusion Method

2013 ◽  
Vol 12 (4) ◽  
pp. 749-755 ◽  
Author(s):  
Shen Yu ◽  
Ren Enen ◽  
Dang Jian-Wu ◽  
Wang Guo-Hua ◽  
Feng Xin
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Medical Image Fusion ◽  
Image Fusion Method

Multi-Modal Medical Image Fusion Using 3-Stage Multiscale Decomposition and PCNN with Adaptive Arguments

2021 ◽  
Author(s):  
Mummadi Gowthami Reddy ◽  
Palagiri Veera Narayana Reddy ◽  
Patil Ramana Reddy
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Technological Development ◽  
Low Frequency ◽  
Fusion Rule ◽  
Multiscale Decomposition ◽  
Frequency Bands ◽  
Coupled Neural Networks ◽  
Medical Image Fusion

In the current era of technological development, medical imaging plays an important role in many applications of medical diagnosis and therapy. In this regard, medical image fusion could be a powerful tool to combine multi-modal images by using image processing techniques. But, conventional approaches failed to provide the effective image quality assessments and robustness of fused image. To overcome these drawbacks, in this work three-stage multiscale decomposition (TSMSD) using pulse-coupled neural networks with adaptive arguments (PCNN-AA) approach is proposed for multi-modal medical image fusion. Initially, nonsubsampled shearlet transform (NSST) is applied onto the source images to decompose them into low frequency and high frequency bands. Then, low frequency bands of both the source images are fused using nonlinear anisotropic filtering with discrete Karhunen–Loeve transform (NLAF-DKLT) methodology. Next, high frequency bands obtained from NSST are fused using PCNN-AA approach. Now, fused low frequency and high frequency bands are reconstructed using NSST reconstruction. Finally, band fusion rule algorithm with pyramid reconstruction is applied to get final fused medical image. Extensive simulation outcome discloses the superiority of proposed TSMSD using PCNN-AA approach as compared to state-of-the-art medical image fusion methods in terms of fusion quality metrics such as entropy (E), mutual information (MI), mean (M), standard deviation (STD), correlation coefficient (CC) and computational complexity.

scholarly journals The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Guocheng Yang ◽  
Meiling Li ◽  
Leiting Chen ◽  
Jie Yu
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Low Frequency ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Medical Image Fusion ◽  
Gaussian Density ◽  
Generalized Gaussian Density ◽  
Fusion Scheme ◽  
Jensen Shannon Divergence

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices.

DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

2021 ◽  
Vol 12 (4) ◽  
pp. 78-97
Author(s):  
Hassiba Talbi ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Weighted Average ◽  
Optimization Method ◽  
Absolute Maximum ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion 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.

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 Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zhaisheng Ding ◽  
Dongming Zhou ◽  
Rencan Nie ◽  
Ruichao Hou ◽  
Yanyu Liu
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Structural Features ◽  
Functional Information ◽  
Convolutional Network ◽  
Structure Information ◽  
Medical Image Fusion ◽  
Research Challenge

Computed tomography (CT) images show structural features, while magnetic resonance imaging (MRI) images represent brain tissue anatomy but do not contain any functional information. How to effectively combine the images of the two modes has become a research challenge. In this paper, a new framework for medical image fusion is proposed which combines convolutional neural networks (CNNs) and non-subsampled shearlet transform (NSST) to simultaneously cover the advantages of them both. This method effectively retains the functional information of the CT image and reduces the loss of brain structure information and spatial distortion of the MRI image. In our fusion framework, the initial weights integrate the pixel activity information from two source images that is generated by a dual-branch convolutional network and is decomposed by NSST. Firstly, the NSST is performed on the source images and the initial weights to obtain their low-frequency and high-frequency coefficients. Then, the first component of the low-frequency coefficients is fused by a novel fusion strategy, which simultaneously copes with two key issues in the fusion processing which are named energy conservation and detail extraction. The second component of the low-frequency coefficients is fused by the strategy that is designed according to the spatial frequency of the weight map. Moreover, the high-frequency coefficients are fused by the high-frequency components of the initial weight. Finally, the final image is reconstructed by the inverse NSST. The effectiveness of the proposed method is verified using pairs of multimodality images, and the sufficient experiments indicate that our method performs well especially for medical image fusion.

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