Medical image fusion scheme using complex contourlet transform based on PCA

Abstract

We present an efficient method for the fusion of medical captured images using different modalities that enhances the original images and combines the complementary information of the various modalities. The contourlet transform has mainly been employed as a fusion technique for images obtained from equal or different modalities. The limitation of directional information of dual-tree complex wavelet (DT-CWT) is rectified in dual-tree complex contourlet transform (DT-CCT) by incorporating directional filter banks (DFB) into the DT-CWT. The DT-CCT produces images with improved contours and textures, while the property of shift invariance is retained. To improve the fused image quality, we propose a new method for fusion rules based on principle component analysis (PCA) which depend on frequency component of DT-CCT coefficients (contourlet domain). For low frequency components, PCA method is adopted and for high frequency components, the salient features are picked up based on local energy. The final fusion image is obtained by directly applying inverse dual tree complex contourlet transform (IDT-CCT) to the fused low and high frequency components. The experimental results showed that the proposed method produces fixed image with extensive features on multimodality.

Medical Image Fusion based on Shearlets and Human Feature Visibility

“…The multimodal medical images has been provided by Hospital Universiti Sains Malaysia (HUSM) [1]. Figure 3, shows the original multimodality image dataset 1, 2, 3 and 4.…”

“…Medical image fusion is the procedure of combining complementary information of a particular organ focused by the different types of modalities [1]. The medical images can include X-ray, computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance angiography (MRA), and positron emission tomography (PET) images.…”

“…Recently, a theory for multidimensional data has been developed to provide higher directional sensitivity than wavelets. Extensive researches have been conducted on image fusion techniques, and various fusion algorithms for medical image have been developed depending on the merging stage [7][8][9][10][11].…”

A Survey on Multimodal Medical Image Fusion

“…There are different mathematical transforms on features that can enhance the performance of the image fusion. For example, combination of complex contourlet transform with wavelet has been shown to result in robust image fusion [95,96]. Transforms based methods are also applied for liver diagnosis [99], risk factor fusion [100], prediction of multifactorial diseases [100], parametric classification [100], local image analysis [101], multimodality image fusion [102,103,95,104,96,81].…”

“…There are several methods that are based on dimensionality reduction techniques such as independent component analysis (ICA) [86,93] and principal component analysis (PCA) [94][95][96][97]. These dimensionality reduction techniques often find their use as feature processing methods and are used in combination with techniques such as ones based on wavelets [86].…”