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Multimodal Medical Volumetric Data Fusion Using 3 D Discrete Shearlet Transform and Global to Local Rule
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Multimodal Medical Volumetric Data Fusion Using 3-D Discrete Shearlet Transform and Global-to-Local Rule

Category : Image Processing


Sub Category : BIOMEDICAL


Project Code : IMP20


Project Abstract

The fusion of three-dimensional (3-D) MRI slices must account for the information not only within the given slice but also the adjacent slices. A fusion method is developed in 3-D Shearlet space to overcome the drawback. On the other hand, the popularly used average–maximum fusion rule can capture only the local information but not any of the global information for it is implemented in a local window region. Thus, a global-to-local fusion rule is proposed. We firstly show the 3-D Shearlet coefficients of the high-pass sub bands are highly non-Gaussian. Then, we show this heavy-tailed phenomenon can be modeled by the generalized Gaussian density (GGD) and the global information between two sub bands can be described by the Kullback–Leibler distance (KLD) of two GGDs. The finally fused global information can be selected according to the asymmetry of the KLD.

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:

          Wavelet-like tools decompose the source mages into only three high-pass sub bands, and the limited high-pass sub bands.

          The MSD coefficients only know the local relationship in a small region but not any of the global relationship between the two corresponding high-pass sub bands.

PROPOSED CONCEPT:

          Shearlet transform provides better image representations since the source images can be decomposed into more than three high-pass sub bands in each level.

          Global relationship between two high-pass sub bands can be described by the Kullback–Leibler distance (KLD).

EXISTING TECHNIQUE:

          Laplacian pyramid transform

          Wavelet transform

PROPOSED ALGORITHM:

          3-D Shearlet Transform Technique

          Kullback–Leibler distance Algorithm

                                                       

TECHNIQUE DEFINITION:

          A Laplacian pyramid is a technique in image processing and uses the concept of pyramids.

          The fundamental idea of wavelet transforms is that the transformation should allow only changes in time extension, but not shape.

ALGORITHM DEFINITION:

          The Kullback Leibler distance (KL-distance) is a natural distance function from a "true" probability distribution to a "target" probability distribution.

          Shearlet are a multiscale framework which allows to efficiently encoding anisotropic features in multivariate problem classes.

DRAWBACKS:

          Only implemented in two dimensional (2-D) spaces.

          Only three high pass sub bands in each level by the wavelet transform, losing the directional sensitivity.

 

ADVANTAGES:

          Our method is applied in the 3-D Shearlet transform space.

          More features information and directional sensitivity in different sub bands can be captured.


 
 
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