Fusion of 3-D medical image gradient domain based on detail-driven and directional structure tensor

2020 ◽  
Vol 28 (5) ◽  
pp. 1001-1016
Author(s):  
Yu Wang ◽  
Yuanjun Wang
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Modern Medicine ◽  
Structure Tensor ◽  
Gradient Field ◽  
Medical Image Fusion ◽  
Multi Scale ◽  
Traditional Structure ◽  
Guided Filtering ◽  
Gradient Domain

BACKGROUND: Multi-modal medical image fusion plays a crucial role in many areas of modern medicine like diagnosis and therapy planning. OBJECTIVE: Due to the factor that the structure tensor has the property of preserving the image geometry, we utilized it to construct the directional structure tensor and further proposed an improved 3-D medical image fusion method. METHOD: The local entropy metrics were used to construct the gradient weights of different source images, and the eigenvectors of traditional structure tensor were combined with the second-order derivatives of image to construct the directional structure tensor. In addition, the guided filtering was employed to obtain detail components of the source images and construct a fused gradient field with the enhanced detail. Finally, the fusion image was generated by solving the functional minimization problem. RESULTS AND CONCLUSION: Experimental results demonstrated that this new method is superior to the traditional structure tensor and multi-scale analysis in both visual effect and quantitative assessment.

Medical Image Fusion in Gradient Domain with Structure Tensor

2016 ◽  
Vol 6 (5) ◽  
pp. 1314-1318 ◽  
Author(s):  
Zhenyi Jin ◽  
Yuanjun Wang ◽  
Zengai Chen ◽  
Shengdong Nie
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Structure Tensor ◽  
Medical Image Fusion ◽  
Gradient Domain

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.

Intelligent multimodal medical image fusion with deep guided filtering

2020 ◽  
Author(s):  
B. Rajalingam ◽  
Fadi Al-Turjman ◽  
R. Santhoshkumar ◽  
M. Rajesh
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Guided Filtering

scholarly journals Medical Image Fusion Based on Sparse Representation and Guided Filtering

2019 ◽  
Vol 1302 ◽  
pp. 022045
Author(s):  
Sa Huang ◽  
Guangyu Chu ◽  
Yifan Fei ◽  
Xiaoli Zhang ◽  
Hailiang Wang
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Guided Filtering

scholarly journals Medical image fusion based on variational and nonlinear structure tensor

2018 ◽  
Vol 189 ◽  
pp. 10021
Author(s):  
Xiaobei Wang ◽  
Rencan Nie ◽  
Xiaopeng Guo
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Energy Functional ◽  
Structure Tensor ◽  
Fusion Method ◽  
Edge Information ◽  
Medical Image Fusion ◽  
Fused Image ◽  
New Energy ◽  
Fusion Methods

Medical image fusion plays an important role in detection and treatment of disease. Although numerous medical image fusion methods have been proposed, most of them decrease the contrast and lose the image information. In this paper, a novel MRI and CT image fusion method is proposed combining rolling guidance filter, structure tensor, and nonsubsampled shearlet transform (NSST). First, the rolling guidance filter and the sum-modified laplacian (SML) operator are introduced in the algorithm to construct the weight maps in non-linear domain, then the fused gradient is firstly obtained by a new weighted structure tensor fusion method, and the fused image is firstly acquired in NSST domain, finally, a new energy functional is defined to constrain the gradient and pixel information of the final fused image close to the pre-fused gradient and the pre-fused image, experimental results show that the proposed method can retain the edge information of source images effectively and preserve the reduction of contrast.

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