Medical Image Condensation Based on Wavelet Transform

2015 ◽  
Vol 719-720 ◽  
pp. 978-981
Author(s):  
Xu Cao ◽  
Hua Xun Zhang
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Medical Image ◽  
Wavelet Decomposition ◽  
Ultrasonic Imaging ◽  
Three Dimensional ◽  
Low Frequency ◽  
Frequency Region ◽  
Vacuum Region ◽  
Image Use

This paper based on the peculiarity of wavelet transform that its transform only in vacuum region and frequency region, decompose image use the theory of wavelet, obtain a series sub-image of different resolution ratio. The value of higeresolution ratio sub-image is all verge on 0, the phenomenon is more obviously in high frequency, so that, the mainly proportion is low frequency to a image. Use wavelet decomposition get rid of the high frequency, only reservation low frequency, to realize the aim of condensation image. Through the simulation of contradistinctive image of cerebra framework remotion between three-dimensional ultrasonic imaging in course of OPS and MIR preceding OPS validated the feasibility by Matlab.

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 Medical image fusion based on quaternion wavelet transform

2020 ◽  
Vol 14 ◽  
pp. 174830262093129
Author(s):  
Zhang Zhancheng ◽  
Luo Xiaoqing ◽  
Xiong Mengyu ◽  
Wang Zhiwen ◽  
Li Kai
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Frequency Component ◽  
High Frequency Component ◽  
Medical Image Fusion ◽  
Quaternion Wavelet Transform ◽  
Fused Image

Medical image fusion can combine multi-modal images into an integrated higher-quality image, which can provide more comprehensive and accurate pathological information than individual image does. Traditional transform domain-based image fusion methods usually ignore the dependencies between coefficients and may lead to the inaccurate representation of source image. To improve the quality of fused image, a medical image fusion method based on the dependencies of quaternion wavelet transform coefficients is proposed. First, the source images are decomposed into low-frequency component and high-frequency component by quaternion wavelet transform. Then, a clarity evaluation index based on quaternion wavelet transform amplitude and phase is constructed and a contextual activity measure is designed. These measures are utilized to fuse the high-frequency coefficients and the choose-max fusion rule is applied to the low-frequency components. Finally, the fused image can be obtained by inverse quaternion wavelet transform. The experimental results on some brain multi-modal medical images demonstrate that the proposed method has achieved advanced fusion result.

Research of Wireless Transmission Based on Pulse Signal

2013 ◽  
Vol 791-793 ◽  
pp. 1965-1969
Author(s):  
Kai Sheng Zhang ◽  
Hui Liu ◽  
Wei Tang
Keyword(s):  
Wavelet Transform ◽  
Personal Computer ◽  
High Frequency ◽  
Wavelet Decomposition ◽  
Low Frequency ◽  
Pulse Signal ◽  
Wireless Transmission ◽  
Baseline Correction ◽  
Pulse Sensor ◽  
Computer Analyses

The system uses dual MCU design to achieve the acquisition and transmission of pulse signal without affecting the users sleeping process. The pulse signal is collected by infrared pulse sensor, and is wireless transferred to the personal computer by JF24C after the process of amplification, baseline correction and so on. Then personal computer analyses the signal using the method of wavelet transform algorithm. With wavelet decomposition, pulse signal can be decomposed into different scales of wavelet coefficients and recomposed according to the low-frequency coefficient of the bottom and high-frequency coefficients of various layers. During the process of wave recomposition , the coefficients related to noise can be rejected while the valuable information in the pulse signal maintained, and all the processes achieve the restoration of pulse signal. What is more, after simulating the wavelet transform algorithm using MATLAB, the consequences of this simulation indicate that multiple interferences can be effectively suppressed while keeping the pulse signal undistorted and it sustains the relevant characteristics of pulse signal.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

Anderson-Stuart Model to Analyze AC and DC Conductivity of Lithium Zinc-Silicate Glass / Glass-Ceramics

2007 ◽  
Vol 280-283 ◽  
pp. 919-924
Author(s):  
M.S. Jogad ◽  
V.K. Shrikhande ◽  
A.H. Dyama ◽  
L.A. Udachan ◽  
Govind P. Kothiyal
Keyword(s):  
High Frequency ◽  
Glass Ceramics ◽  
Low Frequency ◽  
Frequency Region ◽  
Dc Conductivity ◽  
Frequency Range ◽  
High Frequency Region ◽  
Dc Conductivities ◽  
Conductivity Variation ◽  
Different Temperatures

AC and DC conductivities have been measured by using the real (e¢) and imaginary (e¢¢) parts of the dielectric constant data of glass and glass-ceramics (GC) at different temperatures in the rage 297-642K and in the frequency range 100 Hz to 10 MHz. Using Anderson –Stuart model, we have calculated the activation energy, which is observed to be lower than that of the DC conductivity. The analysis for glass/glass-ceramics indicates that the conductivity variation with frequency exhibits an initial linear region followed by nonlinear region with a maximum in the high-frequency region. The observed frequency dependence of ionic conductivity has been analyzed within the extended Anderson–Stuart model considering both the electrostatic and elastic strain terms. In glass/glassceramic the calculations based on the Anderson-Stuart model agree with the experimental observations in the low frequency region but at higher frequencies there is departure from measured data.

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

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 .

A New Infrared Image Magnification and Enhancement Method Based on Wavelet Transform

2012 ◽  
Vol 198-199 ◽  
pp. 238-243 ◽  
Author(s):  
Wen Sheng Guo ◽  
Feng Chen ◽  
Zhao You Sun ◽  
Xi Jun Wang
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Infrared Image ◽  
Low Frequency ◽  
High Energy ◽  
Low Energy ◽  
Image Magnification ◽  
Edge Profile ◽  
Enhancement Method ◽  
Image Edge

The traditional image magnify method usually have some defects on details. This paper gives a new infrared image magnification and enhancement method which is based on wavelet reconstruction and gradation segment. In this method, first of all, make wavelet transform on the image, get the high-frequency coefficient. Apply the Newton differential algorithm enhance the high-frequency coefficient as the high-frequency part of the magnified image, treat the original image as the low-frequency part , make the wavelet reconstruction ,then get the magnified image. To enhance the magnified image, according to the double gray threshold, segment the image into high gray segment corresponding to target, low gray segment corresponding to background, and middle gray segment corresponding to transition sector. Then, make linear extension to them respectively; the result is the magnified image. Experiments indicate, this method is effective on distinguishing high-energy target from low-energy target (the low-energy target is the primary one) and displaying the details of image(edge profile of the bomb).

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.

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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