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2016 | OriginalPaper | Buchkapitel

Medical Image Fusion in Curvelet Domain Employing PCA and Maximum Selection Rule

verfasst von : Himanshi, Vikrant Bhateja, Abhinav Krishn, Akanksha Sahu

Erschienen in: Proceedings of the Second International Conference on Computer and Communication Technologies

Verlag: Springer India

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Abstract

Curvelet transform achieves a compact representation of edges and curved shapes in the image, which other techniques like wavelets and ridgelets are not able to represent. This property of curvelet transform facilitates the retrieval of complementary information from medical images for precise and efficient clinical diagnosis. This paper presents a combination of curvelet transform along with principal component analysis (PCA) and maximum selection rule as an improved fusion approach for MRI and CT-scan. The proposed fusion approach involves image decomposition using curvelet transform followed by application of PCA for dimensionality reduction and the selection of maximum matrix to select only the relevant information in the images. Fusion factor (FF) and structural similarity index (SSIM) are used 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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Metadaten
Titel
Medical Image Fusion in Curvelet Domain Employing PCA and Maximum Selection Rule
verfasst von
Himanshi
Vikrant Bhateja
Abhinav Krishn
Akanksha Sahu
Copyright-Jahr
2016
Verlag
Springer India
DOI
https://doi.org/10.1007/978-81-322-2517-1_1