Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain.

Liangliang Li,Hongbing Ma

doi:10.3390/e23050591

Abstract

Multimodal medical image fusion aims to fuse images with complementary multisource information. In this paper, we propose a novel multimodal medical image fusion method using pulse coupled neural network (PCNN) and a weighted sum of eight-neighborhood-based modified Laplacian (WSEML) integrating guided image filtering (GIF) in non-subsampled contourlet transform (NSCT) domain. Firstly, the source images are decomposed by NSCT, several low- and high-frequency sub-bands are generated. Secondly, the PCNN-based fusion rule is used to process the low-frequency components, and the GIF-WSEML fusion model is used to process the high-frequency components. Finally, the fused image is obtained by integrating the fused low- and high-frequency sub-bands. The experimental results demonstrate that the proposed method can achieve better performance in terms of multimodal medical image fusion. The proposed algorithm also has obvious advantages in objective evaluation indexes VIFF, QW, API, SD, EN and time consumption.

Highlights

In recent years, numerous medical image processing algorithms are being extensively used for visualizing complementary information
Section,totoexplore explorethe theeffectiveness effectiveness proposed multimodal medical of of thethe proposed multimodal medical imimage fusion algorithm, we evaluate the method on the two public datasets http://www
A practical multimodal medical image fusion algorithm based on pulse coupled neural network (PCNN)

Summary

Introduction

Numerous medical image processing algorithms are being extensively used for visualizing complementary information. Medical image fusion is a very effective technique in combining the important information obtained from the multimodal images into one single composite image and enhance the diagnostic accuracy [1,2]. Medical images can be divided into the following categories: Computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission. There is no single imaging method that can reflect the complete tissue information; medical image fusion technology can retain the diagnostic information of input image to the maximum extent [3,4]. We mainly discuss the application of multimodal medical image fusion

Methods

Results

Conclusion