Multi-Mode Medical Image Fusion Algorithm Based on Principal Component Analysis

2009 ◽  
Author(s):  
Hao-quan Wang ◽  
Hao Xing
Keyword(s):  
Principal Component Analysis ◽  
Image Fusion ◽  
Medical Image ◽  
Principal Component ◽  
Component Analysis ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Multi Mode

Multi-Modal Medical Image Fusion Using RGB-Principal Component Analysis

2016 ◽  
Vol 6 (6) ◽  
pp. 1349-1356 ◽  
Author(s):  
Qamar Nawaz ◽  
Xiao Bin ◽  
Li Weisheng ◽  
Du Jiao ◽  
Isma Hamid
Keyword(s):  
Principal Component Analysis ◽  
Image Fusion ◽  
Medical Image ◽  
Principal Component ◽  
Component Analysis ◽  
Medical Image Fusion

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.

Remote sensing image fusion via RPCA and adaptive PCNN in NSST domain

2018 ◽  
Vol 16 (05) ◽  
pp. 1850037 ◽  
Author(s):  
Kang Zhang ◽  
Yongdong Huang ◽  
Cheng Zhao
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Image Fusion ◽  
High Frequency ◽  
Principal Component ◽  
Fusion Rule ◽  
Remote Sensing Image ◽  
Component Analysis ◽  
Fusion Algorithm ◽  
Remote Sensing Image Fusion

In order to improve fused image quality of multi-spectral (MS) image and panchromatic (PAN) image, a new remote sensing image fusion algorithm based on robust principal component analysis (RPCA) and non-subsampled shearlet transform (NSST) is proposed. First, the first principle component PC1 of MS image is extracted via principal component analysis (PCA). Then, the component PC1 and PAN image are decomposed by NSST to get the low and high frequency subbands, respectively. For the low frequency subband, the sparse matrix of PAN image by RPCA decomposition is used to guide the fusion rule; for the high frequency subbands, the fusion rule employed is based on adaptive PCNN model. Finally, the fusion image is obtained by inverse NSST transform and inverse PCA transform. The experimental results illustrate that the proposed fusion algorithm outperforms other classical fusion algorithms (PCA, Curvelet, NSCT, NSST and NSCT-PCNN) in terms of visual quality and objective evaluation in whole, and achieve better fusion performance.

scholarly journals Image Fusion Based on Principal Component Analysis and Slicing Image Transformation

2018 ◽  
Vol 210 ◽  
pp. 04020
Author(s):  
Leonardo Acho ◽  
Pablo Buenestado
Keyword(s):  
Principal Component Analysis ◽  
Data Fusion ◽  
Image Fusion ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Image Transformation ◽  
Source Information ◽  
Fusion Algorithm ◽  
Neural Data

Image fusion deals with the ability to integrate data from image sensors at different instants when the source information is uncertain. Although there exist many techniques on the subject, in this paper, we develop two originative techniques based on principal component analysis and slicing image transformation to efficiently fuse a small set of noisy images. For instance, in neural data fusion, this approach requires a considerable number of corrupted images to efficiently produce the desired outcome and also requiring a considerable computing time because of the dynamics involved in the fusion data process. In our approaches, the computation time is considerably smaller. This results appealing to increasing feasibility, for instance, in remote sensing or wireless sensor network. Moreover, and according to our numerical experiments, when our methods are compared against the neural data fusion algorithm, they present better performance.

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.

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