Medical Image Fusion Based on NonSubsampled Shearlet Transform and Parameter-Adaptive Pulse-Coupled Neural Network

Abstract

In order to improve the fusion accuracy of CT and MRI images, a new medical image fusion method with parameter-adaptive pulse-coupled neural network in nonsubsampled shearlet transform domain is proposed. In the proposed method, the NSST decomposition is first performed on the source images to obtain one low-frequency sub-band and a series of high-frequency sub-bands. Secondly, the high-frequency sub-bands are fused by a PAPCNN model, in which all the PCNN parameters can be adaptively estimated by the input sub-bands. The low-frequency sub-bands adopt an energy attribute-based fusion strategy, which is more conducive to preserving the complete basic information. Finally, the fused image is reconstructed by performing inverse NSST on the fused high-frequency and low-frequency sub-bands. The experimental results demonstrate that the fused images obtained in this paper have clear contours, high contrast, better preservation of detailed texture, and better results have been achieved in objective indicators such as average gradient, entropy, and peak signal-to-noise ratio.