Medical Image Fusion via PCNN Based on Edge Preservation and Improved Sparse Representation in NSST Domain

Abstract

Medical image fusion integrates image features of different modalities to provide comprehensive information for clinical diagnosis, treatment planning, and image-guided surgery. The information of the fused image is richer and clearer, which makes up for the defect of the single-mode medical image and retains the characteristic information of the source image. This paper proposes a novel multi-modality medical image fusion method based on gray medical images and color medical images. In the proposed method, the nonsubsampled shearlet transform (NSST) method is first used to decompose the source image into a low frequency sub-band and several high frequency sub-bands. The improved sparse representation is utilized to fuse the low frequency sub-band, which can remove the detail features through the sobel operator and the guided filter to improve the ability to preserve energy effectively. Meanwhile, the high frequency sub-bands are fused by a pulse coupled neural network (PCNN) based on edge preservation. This method fully considers the imaging characteristics of different medical modalities, which can process edge information well and process image details better. Finally, the fused low frequency sub-band and high frequency sub-bands are inversely transformed to obtain the final fused image. Seven different categories of medical images and seven fusion methods are used to verify the effectiveness of the proposed method. In addition, the medical images of three different modalities are merged to testify the influence of the fusion sequence. The experimental results show that the proposed method is superior to existing state-of-art methods in subjective visual performance and objective evaluation.