Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

  • Authors

    • M Shyamala Devi
    • P Balamurugan
    2018-05-29
    https://doi.org/10.14419/ijet.v7i2.31.13432
  • Multimodal image, PSNR, RMSE, NSCT, Gabor Filter.

  • Image processing technology requires moreover the full image or the part of image which is to be processed from the user’s point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edge-like features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work

     .

  • References

    1. [1] Petrovic VS & Xydeas CS, “Gradient-based multiresolution image fusionâ€, IEEE Trans. Image Process., Vol.13, no.2, (2004), pp.228–237, Feb.

      [2] Li H, Manjunath BS & Mitra SK, “Multi sensor image fusion using the wavelet transformâ€, Graph Models Image Process., Vol.57, No.3, (1995), pp.235–245.

      [3] Bhatnagar G & Raman B, “A new image fusion technique based on directive contrastâ€, Electron. Lett. Comput. Vision Image Anal., Vol. 8, No.2, (2009), pp.18–38.

      [4] Zhang Q & Guo BL, “Multi focus image fusion using the non subsampled Contourlet transformâ€, Signal Process., Vol.89, No.7, (2009), pp.1334–1346.

      [5] Ardizzone E, Pirrone R, & Orazio OG, “Fuzzy C-Means Segmentation on Brain MR Slices Corrupted by RF-Inhomogeneityâ€, Proc. The 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory, (2007), pp.378-384.

      [6] Bianrgi PM, Ashtiyani M & Asadi S, “MRI Segmentation Using Fuzzy C-means Clustering Algorithm Basis Neural Networkâ€, In Proc. ICTT A 3rd International Conference on Information and Communication Technologies: From Theory to Applications, (2008), pp.1-5.

  • Downloads

  • How to Cite

    Shyamala Devi, M., & Balamurugan, P. (2018). Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion. International Journal of Engineering & Technology, 7(2.31), 165-169. https://doi.org/10.14419/ijet.v7i2.31.13432