Abstract
A new method of medical image fusion is proposed in this paper, which is based on human visual models and IHS color space. Retina-inspired difference of Gaussian model is adopted to enhance the spatial information of anatomical images. Also, 2D Log-Gabor model of primary visual cortex is used to enhance the spectrum information of functional images. The statistical analyses tools such as average gradient and entropy are demonstrated that the proposed algorithm does considerably increase spatial information content and reduce the color distortion compared to the counterpart fusion methods. In the proposed fused images the color information is least distorted, the spatial details are as clear as the original anatomical images, and the integration of color and spatial features was normal.
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Jingyi, Q., Yunfei, J., Ying, D. (2012). A Medical Image Fusion Method Based on Visual Models. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_29
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DOI: https://doi.org/10.1007/978-3-642-31362-2_29
Publisher Name: Springer, Berlin, Heidelberg
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