scholarly journals Log-Gabor Energy Based Multimodal Medical Image Fusion in NSCT Domain

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Yong Yang ◽  
Song Tong ◽  
Shuying Huang ◽  
Pan Lin
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Discrete Wavelet ◽  
Fusion Method ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion ◽  
Log Gabor

Multimodal medical image fusion is a powerful tool in clinical applications such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning. In this paper, a novel nonsubsampled Contourlet transform (NSCT) based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena. The source medical images are initially transformed by NSCT followed by fusing low- and high-frequency components. The phase congruency that can provide a contrast and brightness-invariant representation is applied to fuse low-frequency coefficients, whereas the Log-Gabor energy that can efficiently determine the frequency coefficients from the clear and detail parts is employed to fuse the high-frequency coefficients. The proposed fusion method has been compared with the discrete wavelet transform (DWT), the fast discrete curvelet transform (FDCT), and the dual tree complex wavelet transform (DTCWT) based image fusion methods and other NSCT-based methods. Visually and quantitatively experimental results indicate that the proposed fusion method can obtain more effective and accurate fusion results of multimodal medical images than other algorithms. Further, the applicability of the proposed method has been testified by carrying out a clinical example on a woman affected with recurrent tumor images.

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.

Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy

Oncology ◽  
2017 ◽  
pp. 519-541
Author(s):  
Satishkumar S. Chavan ◽  
Sanjay N. Talbar
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Frequency Domain ◽  
Medical Image ◽  
Treatment Plan ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion ◽  
Daubechies Complex Wavelet Transform ◽  
Fused Image ◽  
Complex Wavelet

The process of enriching the important details from various modality medical images by combining them into single image is called multimodality medical image fusion. It aids physicians in terms of better visualization, more accurate diagnosis and appropriate treatment plan for the cancer patient. The combined fused image is the result of merging of anatomical and physiological variations. It allows accurate localization of cancer tissues and more helpful for estimation of target volume for radiation. The details from both modalities (CT and MRI) are extracted in frequency domain by applying various transforms and combined them using variety of fusion rules to achieve the best quality of images. The performance and effectiveness of each transform on fusion results is evaluated subjectively as well as objectively. The fused images by algorithms in which feature extraction is achieved by M-Band Wavelet Transform and Daubechies Complex Wavelet Transform are superior over other frequency domain algorithms as per subjective and objective analysis.

A Novel Fusion Rule for Medical Image Fusion in Complex Wavelet Transform Domain

2016 ◽  
Vol 16 (04) ◽  
pp. 1650022 ◽  
Author(s):  
Deepak Gambhir ◽  
Meenu Manchanda
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Medical Image ◽  
Fusion Rule ◽  
Complex Wavelet Transform ◽  
Fusion Rules ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Complex Wavelet ◽  
Complex Coefficients

Medical image fusion is being used at large by clinical professionals for improved diagnosis and treatment of diseases. The main aim of image fusion process is to combine complete information from all input images into a single fused image. Therefore, a novel fusion rule is proposed for fusing medical images based on Daubechies complex wavelet transform (DCxWT). Input images are first decomposed using DCxWT. The complex coefficients so obtained are then fused using normalized correlation based fusion rule. Finally, the fused image is obtained by inverse DCxWT with all combined complex coefficients. The performance of the proposed method has been evaluated and compared both visually and objectively with DCxWT based fusion methods using state-of art fusion rules as well as with existing fusion techniques. Experimental results and comparative study demonstrate that the proposed fusion technique generates better results than existing fusion rules as well as with other fusion techniques.

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