Multispectral medical image fusion scheme based on hybrid contourlet and shearlet transform domains

Multimodal medical image sensor fusion has revolutionized the medical analysis by improving the precision of computer assisted diagnosis. This is incorporated by highlighting the complementary information while minimizing the redundant content in the fused images from various biomedical sensors like MRI, Computed Tomography, and Positron Emission Tomography/Single-Photon Emission Computerized Tomography. Multispectral image fusion is a special case of multimodal fusion which serves to encompass both spatial and spectral details in the fused image. This paper presents a hybrid sub-band decomposition scheme for multispectral image fusion comprising of non-subsampled contourlet transform and shearlet transform domains. The pre-processing stage involves color transformation of an input multispectral image from red-green-blue to YIQ color space. Thereafter, both the source images (i.e., panchromatic and multispectral images) after sub-band decomposition are processed via the application of contrast enhancement, weighted-principal component analysis, and max-max algorithms. The low frequency coefficients are processed via phase congruency whereas a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The objective assessment of image quality has been carried out using various reference and no-reference based performance metrics. The distinguishing fusion response of the proposed hybrid scheme has been validated by the comparisons done with the other fusion approaches.