Abstract
In this paper, registration-based multimodal medical image fusion process implemented using 3D shearlet transform. Here, 3D MRI simulated slices with three different sets like T1-weighted, T2-weighted, proton density (Pd) are fused to view the abnormalities. As well as magnetic resonance imaging (MRI) and single-photon emission computed tomography (SPECT) images are fused to obtain more useful information and diseases like Cavernous angioma, Hungtinton’s chorea are evaluated. 3D shearlet coefficients of the high-pass subbands are highly non-Gaussian and they are modeled into generalized Gaussian distribution (GGD) using heavy-tailed phenomenon. Kullback–Leibler distance is used to obtain local and global information between two high-pass bands and the images are fused by registration. By fusing memory required for storage is also reduced.
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Asaithambi, N., Kayalvizhi, R., Selvi, W. (2016). 3D Multimodal Medical Image Fusion and Evaluation of Diseases. In: Suresh, L., Panigrahi, B. (eds) Proceedings of the International Conference on Soft Computing Systems. Advances in Intelligent Systems and Computing, vol 397. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2671-0_40
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DOI: https://doi.org/10.1007/978-81-322-2671-0_40
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