# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 2 Related Works  
### Image Fusion  
In many image-processing scenarios, high spatial and high spectral information are required in a single image. Due to observational constraints, current instruments cannot obtain complete information. The image fusion technique is proposed to combine all relevant or complementary information from two or more source images into a single image [27]. The fused image has more information that any of the source images.  
Spatial-domain fusion methods directly analyze source images to choose clear pixels, blocks or regions to compose the fused image. Energy of Laplacian and spatial frequency are two typical methods to measure the clarity of pixels or regions. Based on the image clarity measurement, those pixels or regions with higher clarity are selected to construct the fused image. Misalignment and incorrect location of focused objects constrain the performance of spatial-domain fusion methods. For decreasing the constraints, averaging and max pixel schemes perform on a single pixel to generate a fused image. However, the contrast and edge intensity of the fused image may decrease.  
Weighted average method, independent component analysis (ICA) [28], principal component analysis (PCA) [29], and brovey transform [30] are typical spatial-domain fusion methods. Spatial-domain fusion methods do spatial distortions by using localized spatial features, such as spatial gradient, spatial frequency and information entropy. However, the spatial features cannot fully reflect the image information, so spatial-domain fusion methods cannot obtain fused images in good quality.  
To improve the performance of a fused image, scientists have proposed block-based and region-based algorithms. [PERSON] proposed an image fusion method in discrete cosine transform domain, that divided a source image into 8 by 8 pixel blocks first and then employed a contrast measure to merge the divided blocks [31]. [PERSON] proposed a scheme to divide image into blocks and chose the blocks by comparing their spatial frequencies (SF); the fused results are produced by consistency verification [32]. An averaging or max pixel scheme is performed on the single pixel to generate fused image. Although block-based algorithms improve the fusion performance in expressing details of a fused image, the sharpness of the fused image may still be undesirable. Block-based algorithms may cause a block effect, when they are applied to spatial-domain based methods.  
Transform-domain fusion methods first transfer source images into frequency domain and then fuse the corresponding coefficients. Many different mathematical transforms are proposed to enhance the performance of the image fusion. Multi-scale transform (MST) algorithms, as a conventional transformation approach, are used to decompose the source images. Wavelet transform basis, shearlet basis, curvelet basis, dual tree complex wavelet transform basis, non-subsampled contourlet transform (NSCT) basis are usually used for MST-based methods. Without a priori knowledge, it is difficult to select an optimal transform basis. Transform-domain based methods cannot give a general solution to fit all source images. The context of source image is closely related to the performance of transform-domain methods. Pyramid and wavelet transforms are used for multi-resolution image fusion [33]. Combination of complex contourlet transform with wavelet can obtain robust image fusion [34]. Transform-based methods can be applied to liver diagnosis [35], prediction of multifactorial diseases [36], parametric classification [36], and multi-modality image fusion [37; 38].