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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 328))

Abstract

Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of diseases. This paper presents a combination of Principal Component Analysis (PCA) and ridgelet transform as an improved fusion approach for MRI and CT-scan. The proposed fusion approach involves image decomposition using 2D-Ridgelet transform in order to achieve a compact representation of linear singularities. This is followed by application of PCA as a fusion rule to improve upon the spatial resolution. Fusion Factor (FF) and Structural Similarity Index (SSIM) are used as fusion metrics for performance evaluation of the proposed approach. Simulation results demonstrate an improvement in visual quality of the fused image supported by higher values of fusion metrics.

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Correspondence to Abhinav Krishn .

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Krishn, A., Bhateja, V., Himanshi, Sahu, A. (2015). PCA Based Medical Image Fusion in Ridgelet Domain. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-12012-6_52

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  • DOI: https://doi.org/10.1007/978-3-319-12012-6_52

  • Publisher Name: Springer, Cham

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