Abstract
This paper focuses on the evaluation of noisy image fusion for medical images obtained from different modalities. In general, medical images suffer from poor contrast and are corrupted by blur and noise due to the imperfection of image capturing devices. In order to improve the visual and quantitative quality of the fused image, we compare two algorithms with other fusion techniques. The first algorithm is based on Dual Tree Complex Wavelet Transform (DTCWT) while the second is based on Scale Mixing Complex Wavelet Transform (SMCWT). The tested algorithms are using different fusion rules in each one, which leads to a perfect reconstruction of the output (fused image), this combination will create a new method which exploits the advantages of each method separately. DTCWT presents a good directionality since it considers the edge information in six directions and provide approximate shift invariant as well as SM-CWT, the goal of PCA is to extract the most significant features (wavelet coefficients in our case) to improve the spatial resolution. We compared the tested methods visually and quantitatively to recent fusion methods presented in the literature over several sets of medical images at multiple levels of noise. Further, the tested fusion algorithms have been tested up to the important level of Gaussian, salt & pepper and speckle noise (350 test). For the quantitative quality, we used several well-known fusion metrics. The results show that the tested methods outperform each method individually and other algorithms proposed in the literature.
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James, A.P., Dasarathy, B.V.: Medical image fusion: a survey of the state of the art. Inf. Fusion 19, 4–19 (2014)
Bengueddoudj, A., Messali, Z., Mosorov, V.: A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images. J. Innov. Opt. Health Sci. 10, 1750001 (2017)
Bengueddoudj, A., Messali, A.: An efficient algorithm for multimodal medical image fusion based on feature selection and PCA using DTCWT (FSPCA-DTCWT). Med. Technol. J. 2(1), 179–192 (2018)
Ganasala, P., Kumar, V.: CT and MR image fusion scheme in nonsubsampled contourlet transform domain. J. Digit. Imag. 27, 407–418 (2014)
Daneshvar, S., Ghassemian, H.: MRI and PET image fusion by combining IHS and retina-inspired models. Inf. Fusion 11, 114–123 (2010)
Singh, R., Khare, A.: Fusion of multimodal medical images using Daubechies complex wavelet transform – a multiresolution approach. Inf. Fusion 19, 49–60 (2014)
Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.C.: The dual-tree complex wavelet transform. IEEE Sig. Process. Mag. 22, 123–151 (2005)
Remenyi, N., Nicolis, O., Nason, G., Vidakovic, B.: Image denoising with 2D scale-mixing complex wavelet transforms. IEEE Trans. Image Process. 23, 5165–5174 (2014)
Chiorean, L., Vaida, M.-F.: Medical image fusion based on discrete wavelet transform using Java technology. In: Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces, pp. 55–60 (2009)
Rockinger, O.: Image sequence fusion using a shift-invariant wavelet transform. Proc. Int. Conf. Image Process. 3, 288–291 (1997)
Singh, R., Srivastava, R., Prakash, O., Khare, A.: Multimodal medical image fusion in dual tree complex wavelet transform domain using maximum and average fusion rules. J. Med. Imag. Health Inform. 2, 168–173 (2012)
Shreyamsha Kumar, B.K.: Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform. Sig. Image Video Process. 7, 1125–1143 (2013)
Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15, 430–444 (2006)
Crowley, J.L., Martin, J.: Experimental comparison of correlation techniques. In: IAS-4, International Conference on Intelligent Autonomous Systems, Karlsruhe (1995)
Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Inf. Fusion 14, 127–135 (2013)
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Bengueddoudj, A., Messali, Z. (2019). Subjective and Objective Evaluation of Noisy Multimodal Medical Image Fusion Using 2D-DTCWT and 2D-SMCWT. In: Demigha, O., Djamaa, B., Amamra, A. (eds) Advances in Computing Systems and Applications. CSA 2018. Lecture Notes in Networks and Systems, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-98352-3_24
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DOI: https://doi.org/10.1007/978-3-319-98352-3_24
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