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

  • Himanshi
  • Vikrant Bhateja
  • Abhinav Krishn
  • Akanksha Sahu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)

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.

Keywords

Curvelet transform Fusion factor Max fusion rule MRI PCA 

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Copyright information

© Springer India 2016

Authors and Affiliations

  • Himanshi
    • 1
  • Vikrant Bhateja
    • 1
  • Abhinav Krishn
    • 1
  • Akanksha Sahu
    • 1
  1. 1.Department of Electronics and Communication EngineeringShri Ramswaroop Memorial Group of Professional CollegesLucknowIndia

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