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Dr. Shady Yehia AbdElazim Elmashad :: Publications:

Hybrid Medical Image Fusion based on Fast Filtering and Wavelet Analysis
Authors: Shrouk A. El-Masry, Shady Y. El-Mashad, Noha E. El-Attar and Wael A. Awad
Year: 2019
Keywords: image fusion, fast filtering, wavelet transform
Journal: Ninth IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2019
Volume: Not Available
Issue: Not Available
Pages: 167-173
Publisher: IEEE
Local/International: International
Paper Link:
Full paper Shady Yehia AbdElazim Elmashed _Hybrid Medical Image Fusion based on Fast Filtering and Wavelet Analysis.pdf
Supplementary materials Not Available

Within medical imaging, there are various modalities of medical images like CT, X-rays, MRI and other modalities that provide information about a human body in different ways. Each modality has distinctive characteristics that provide various sources of information. Therefore, there are some problems like image comparison such as CT/PET, CT /MRI, and MRI/ PET were usually meet by the clinical treatment and diagnosis. Hence the need to combine the different images' information and this process is known as 'medical image fusion'. In this paper, two techniques for the ‘medical image fusion’ are introduced. The first proposed fusion technique is the combination of the fast filtering with the discrete wavelet transform 'DWT' methods for overcoming the low spatial resolution fused image provided by DWT and preserve the source images' salient features. Where we used the fast filtering method procedures for combining the corresponding 'low-frequency coefficients' to maintain the 'salient features' of the initial images, and the maximum rule with the high-frequency coefficients which lead getting better the resultant image contrast. The second proposed technique is the combination of fast filtering with stationary wavelet transform (SWT) methods, where 'SWT' has the shift-invariant property which enables to overcome the shift-variance DWT's drawback. The performance of the fused output is tested and compared with five of the common fusion methods like the Gradient pyramid, Contrast pyramid, DWT, Fast Filtering, and SWT techniques, using performance parameters: E, SNR, SD, and PSNR.

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