NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency

Abstract

In this article, a novel multimodal medical image fusion (MIF) method based on non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN) is presented. The proposed MIF scheme exploits the advantages of both the NSCT and the PCNN to obtain better fusion results. The source medical images are first decomposed by NSCT. The low-frequency subbands (LFSs) are fused using the 'max selection' rule. For fusing the high-frequency subbands (HFSs), a PCNN model is utilized. Modified spatial frequency in NSCT domain is input to motivate the PCNN, and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image. Finally, inverse NSCT (INSCT) is applied to get the fused image. Subjective as well as objective analysis of the results and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme in fusing multimodal medical images.

A Survey on Multimodal Medical Image Fusion

“…Therefore, NSCT is more suitable for medical image fusion. Although medical image fusion methods based NSCT have achieved good results [10,11,12,13,14], most existing fusion methods neglect the dependencies between subband coefficients at the interscale and intrascale. However, the dependencies between decomposition coefficients commonly exist.…”

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

“…Therefore, NSCT provides better frequency selectivity and an important property of the shift-invariance on account of non-subsampled operation. The size of sub-images decomposed by NSCT is same and image fusion based on NSCT can mitigate the effects of misregistration in the fused images [25]. Therefore, NSCT is more suitable for medical image fusion.…”

Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

“…Pyramidal fusion methods including the laplacian pyramid [7], gradient pyramid [8], ratio-of-low-pass pyramid and the morphological pyramid [9] fail to capture the spatial orientation in the decomposition process; hence cause blocking effects [10]. Mathematical methods including principal component analysis [11,12], intensity-hue saturation [13,14] and the Brovey transform [15] offer better results, but suffer from spectral degradation [16].…”

“…Several Image Fusion (IF) and Medical Image Fusion (MIF) techniques based on PCNN have been proposed by researchers [15][16][17][18][19][20]. The majority of the MIF techniques based on PCNN use the normalized single value of the pixel in the spatial domain or the coefficient in the transform domain as the feeding input to the PCNN which leads to contrast reduction and loss of directional information respectively [19,[21][22][23][24].…”

“…Kavitha and Chellamuthu [18] enhanced the input before feeding it into the PCNN using the ant colony optimization (ACO) technique. Das and Kundu [16] proposed a modified spatial frequency motivated PCNN to fuse the high frequency sub-bands and max selection fusion rule to fuse the low frequency sub-bands. Xiao-Bo et al [20] proposed a spatial frequency motivated pulse coupled neural network to fuse low and high frequency sub-bands.…”