A New Adaptive Image Fusion Technique of CT and MRI Images Based on Dual-Tree Complex Wavelet Transform

2013 ◽  
Vol 411-414 ◽  
pp. 1189-1192 ◽  
Author(s):  
Ling Yan Du ◽  
Yin Jie ◽  
Zhan Xu
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Principal Component ◽  
Visual Quality ◽  
Complex Wavelet Transform ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Image Fusion Technique ◽  
Ct And Mri ◽  
Better Than

In this paper, a new adaptive images fusion algorithm is presented for CT and MRI based on DT-CWT. For fusion, all the source images are decomposed into low and high frequency sub-bands, and then the fusion of low frequency is done by means of Principal Component Analysis (PCA) while for high frequency regional energy algorithm is used. Experiment are carried out on a number of CT and MRT images, results show that the DT-CWT method is better than that of DWT method in terms of quality measures PSNR, NCC and image visual quality.

Novel Infrared and Visible Images Fusion Algorithm Based on NSCT

2014 ◽  
Vol 989-994 ◽  
pp. 3763-3767
Author(s):  
Hai Feng Tan ◽  
Tian Wen Luo ◽  
Jing Jun Zhu ◽  
Guan Zhong Li ◽  
Quan Xi Zhang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Nonsubsampled Contourlet Transform ◽  
Target Image ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Fusion Methods ◽  
Visible Images ◽  
Regional Variance ◽  
Better Than

A novel fusion algorithm based on Nonsubsampled Contourlet Transform (NSCT) is proposed, according to the characteristics of infrared and visible images. Firstly, the registered infrared and visible images from the same scene were transformed by NSCT transforms; then the low frequency coefficient is fused by the combination of local energy and normalised correlation matrix, the high frequency coefficient fusion is fused by regional energy matching with regional variance; finally, the target image is obtained by performing inverse NSCT transforms. experimental results indicate that the proposed algorithm can effectively get more detail information and the fusion performance is dramatically better than traditional fusion methods.

A Novel Fusion Method Using NSCT and PCNN

2013 ◽  
Vol 380-384 ◽  
pp. 3994-3997 ◽  
Author(s):  
Hong Li ◽  
Fen Xia Wu ◽  
Cong E Tan ◽  
Gong Yu Li
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
The Novel ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Weighted Fusion ◽  
Contrast Method ◽  
Simulation Results ◽  
Better Than

An image fusion algorithm by combing Nonsubsampled Contourlet Transform and Pulse Coupled Neural Network is proposed in this paper. Two primitive matched images are decomposed in NSCT domain. The low frequency used the regional energy weighted fusion. The high frequency coefficients are input to the PCNN. Finally, the fused result is obtained through inverse NSCT. The novel algorithm will employ features and contain the most of the energy of image. The simulation results show that the results are much better than the experimental results, which the mutual information and QAB/F parameters are higher than the contrast method of fusion results.

Fusion Algorithm of Infrared and Visible Images Based on Multiresolution Decomposition

2012 ◽  
Vol 239-240 ◽  
pp. 229-232
Author(s):  
Chen Ding
Keyword(s):  
Statistical Model ◽  
Image Fusion ◽  
Frequency Band ◽  
High Frequency ◽  
Wavelet Coefficient ◽  
Low Frequency ◽  
High Frequency Band ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Multiresolution Decomposition

Information redundancy and complementarity are existing between the images obtained by multi-sensor, image fusion can improve the certainty and reliability of the information. Traditional method of image fusion based on multiresolution decomposition is susceptible to high frequency noise, fusion is often ineffective. A image fusion algorithm has been studied based on the wavelet multiresolution decomposition which is regional energy maximum for low-frequency decomposition image, and the bivariate statistical model for high-frequency part. The results show that: in the conditions of Daubechies 3 wavelet basis function, decomposition level 5 multiresolution decomposition, the bivariate statistical model for the high-frequency band is robust to noise based on the joint probability of wavelet coefficient pair - a wavelet coefficient and its parent; in the same time, the regional energy maximum for low-frequency band can be effective on the high-frequency band based on the bivariate statistical model. The fusion image has the biggish contrast, the preferable details, the higher gray level resolution.

An Infrared and Visible Images Fusion Algorithm Based on Wavelet Multi-Resolution Decomposition

2014 ◽  
Vol 989-994 ◽  
pp. 3734-3737
Author(s):  
Li Kun Liu ◽  
Zong Jia Wu
Keyword(s):  
Statistical Model ◽  
Image Fusion ◽  
Frequency Band ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Noise ◽  
High Frequency Band ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Energy Maximum

Image fusion can be effectively utilized to obtain image redundant information from sensors, hereby improving the accuracy and reliability of information. Based on multi-resolution decomposition of the traditional image fusion method is vulnerable to high frequency noise, fusion is often ineffective. An improved image fusion algorithm has been studied based on the wavelet multi-resolution decomposition. The principle of the algorithm is regional energy maximum for low frequency decomposition image, and the bivariate statistical model for high frequency part. Experimental results show that the bivariate statistical model for the high frequency band is robust to noise based on the joint probability of wavelet coefficient in the conditions of Daubechies wavelet basis function with decomposing level 5 multi-resolution decomposition. Simultaneously, the regional energy maximum for low frequency band can be effective on the high frequency band based on the bivariate statistical model. Fusion image have a larger contrast, the preferred details and the higher gray level resolution.

A Novel Adaptive PET/CT Image Fusion Algorithm

2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
High Frequency ◽  
Malignant Tumors ◽  
Low Frequency ◽  
Combination Method ◽  
Ct Image ◽  
Fusion Algorithm ◽  
Pet Ct ◽  
Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

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 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 .

Infrared and visible image fusion using dual-tree complex wavelet transform and convolutional sparse representation

2020 ◽  
Vol 39 (3) ◽  
pp. 4617-4629
Author(s):  
Chengrui Gao ◽  
Feiqiang Liu ◽  
Hua Yan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Low Frequency ◽  
Infrared Images ◽  
Complex Wavelet Transform ◽  
Visible Image ◽  
Visible Images ◽  
Complex Wavelet

Infrared and visible image fusion refers to the technology that merges the visual details of visible images and thermal feature information of infrared images; it has been extensively adopted in numerous image processing fields. In this study, a dual-tree complex wavelet transform (DTCWT) and convolutional sparse representation (CSR)-based image fusion method was proposed. In the proposed method, the infrared images and visible images were first decomposed by dual-tree complex wavelet transform to characterize their high-frequency bands and low-frequency band. Subsequently, the high-frequency bands were enhanced by guided filtering (GF), while the low-frequency band was merged through convolutional sparse representation and choose-max strategy. Lastly, the fused images were reconstructed by inverse DTCWT. In the experiment, the objective and subjective comparisons with other typical methods proved the advantage of the proposed method. To be specific, the results achieved using the proposed method were more consistent with the human vision system and contained more texture detail information.

AN EFFICIENT METHOD FOR CORRECTION OF ECTOPIC BEATS IN R-R INTERVALS

2008 ◽  
Vol 08 (03n04) ◽  
pp. L359-L368 ◽  
Author(s):  
OMER H. COLAK
Keyword(s):  
Efficient Method ◽  
High Frequency ◽  
Low Frequency ◽  
Principal Component ◽  
Sliding Window ◽  
Pulse Frequency ◽  
Pulse Frequency Modulation ◽  
Energy Distributions ◽  
Ectopic Beats ◽  
Average Filter

This study presents an efficient method based on principal component analysis (PCA) to remove ectopic beats in R-R intervals. The method is focused on variation of slopes at each time steps and reconstruction of new time values using initial eigenvectors and new slope values. Obtained results and LF (low frequency) and HF (high frequency) energy distributions were compared with outputs of integral pulse frequency modulation (IPFM) method and sliding window average filter (SWAF).

Multisource Image Fusion Based on DWT and Simplified Pulse Coupled Neural Network

2013 ◽  
Vol 457-458 ◽  
pp. 736-740 ◽  
Author(s):  
Nian Yi Wang ◽  
Wei Lan Wang ◽  
Xiao Ran Guo
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Cortical Model ◽  
Fusion Algorithm ◽  
Global Coupling ◽  
Maximum Selection ◽  
Pulse Synchronization

In this paper, a new image fusion algorithm based on discrete wavelet transform (DWT) and spiking cortical model (SCM) is proposed. The multiscale decomposition and multi-resolution representation characteristics of DWT are associated with global coupling and pulse synchronization features of SCM. Two different fusion rules are used to fuse the low and high frequency sub-bands respectively. Maximum selection rule (MSR) is used to fuse low frequency coefficients. As to high frequency subband coefficients, spatial frequency (SF) is calculated and then imputed into SCM to motivate neural network. Experimental results demonstrate the effectiveness of the proposed fusion method.

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