Medical image fusion based on nonsubsampled shearlet transform and principal component averaging

2019 ◽  
Vol 17 (04) ◽  
pp. 1950023 ◽  
Author(s):  
Tannaz Akbarpour ◽  
Mousa Shamsi ◽  
Sabalan Daneshvar ◽  
Masoud Pooreisa
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Modern Medicine ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Medical Image Fusion ◽  
Planning Methods

Medical image fusion has a crucial role in many areas of modern medicine like diagnosis and therapy planning. Methods based on principal component analysis (PCA) have been extensively used in area of medical image fusion due to their computational simplicity. Methods based on multiresolution analysis are of attraction now due to their ability in extracting image details. A new method is proposed in this paper to benefit from these advantages. For this aim, firstly, images are transformed into multiscale space based on nonsubsampled shearlet transform (NSST). Secondly, principal components and weights of each subband are calculated. Averaging them yields weights necessary for fusion step. Finally, fused image is achieved by merging source images according to weights. Quantitative and qualitative analysis prove outperformance of our methods compared to well-known fusion methods and improvement compared to subsequent best method, in terms of standard deviation [Formula: see text], entropy [Formula: see text], structural similarity [Formula: see text], signal to noise ratio [Formula: see text] and fusion performance metric [Formula: see text].

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.

A Novel Medical Image Fusion Approach Based on Nonsubsampled Shearlet Transform

2019 ◽  
Vol 9 (9) ◽  
pp. 1815-1826 ◽  
Author(s):  
Liangliang Li ◽  
Linli Wang ◽  
Zuoxu Wang ◽  
Zhenhong Jia ◽  
Yujuan Si ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Medical Image Fusion ◽  
Fusion Approach

Multimodal sensor medical image fusion based on nonsubsampled shearlet transform and S-PCNNs in HSV space

2018 ◽  
Vol 153 ◽  
pp. 379-395 ◽  
Author(s):  
Xin Jin ◽  
Gao Chen ◽  
Jingyu Hou ◽  
Qian Jiang ◽  
Dongming Zhou ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Medical Image Fusion

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.

Sign in / Sign up

Export Citation Format

Share Document