Multimodal Medical Image Fusion based on Undecimated Wavelet Transform and Fuzzy Sets
Praneel Kumar Peruru1, Kasa Madhavi2, T. Tirupal3

1Praneel Kumar Peruru, Research Scholar, Department of CSE, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu (Andhra Pradesh), India.
2Kasa Madhavi, Associate Professor, Department of CSE, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu (Andhra Pradesh), India.
3T. Tirupal, Associate Professor, Department of ECE, G Pullaiah College of Engineering and Technology, Kurnool (Andhra Pradesh), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 97-103| Volume-8 Issue-6, April 2019 | Retrieval Number: F3402048619/19©BEIESP
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Abstract: The ideal objective of combining therapeutic pictures is converging of the numerous pictures acquired from multimodalities into a solitary picture for prevalent sickness analysis. A tale calculation for skillful melding of multimodal restorative pictures is proposed in this paper. For combining of restorative pictures, Undecimated Discrete Wavelet Transform domain (UDWT) and Intuitionistic fuzzy sets are used. Dominant of the available fusion techniques are working in light of Discrete Wavelet Transform (DWT). A slight obscuring is seen when DWT is utilized for image fusion. By using UDWT, this blurring is significantly reduced. There is no decimation process in UDWT. Henceforth, wavelet coefficients are processed for every area permitting better identification of prevailing highlights. It is a non-orthogonal multi-resolution decomposition. In the UDWT domain, the low recurrence sub-groups are combined by the Maximum Selection Rule and the high recurrence sub-groups are intertwined by the Modified Spatial Frequency (MSF). Recreations are carried on different therapeutic pictures and diverged from strategies exist up until now. Predominance of the proposed technique is introduced and supported. Combined picture quality is affirmed with number of value measurements i.e., Entropy (H), Spatial Frequency (SF), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Edge-based image fusion metric (QAB/F), Correlation Coefficient (CC), Quality Index (QI) and Structural Similarity (SSIM).
Keyword: Image Fusion, Fuzzy Set Theory, Discrete Wavelet Transform, Medical Image Processing, Wavelet Transform, PSNR.
Scope of the Article: Signal and Image Processing