Medical Image Fusion Based on Feature Extraction and Sparse Representation.

Yin Fei,Song Zongxi,Gao Wei

doi:10.1155/2017/3020461

Abstract

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.

Highlights

Medical imaging attracts more and more attention due to the increasing requirements of clinic investigation and disease diagnosis
A new medical image fusion approach based on Sparse representation (SR) and feature extraction is proposed
There are at least three major improvements compared with the conventional SR based fusion methods

Summary

Introduction

Medical imaging attracts more and more attention due to the increasing requirements of clinic investigation and disease diagnosis. Single multiple modality cannot satisfy the demand of images with high resolution and visualization for disease diagnosis In this regard, medical image fusion is a useful and powerful technique for integrating complementary information from multimodality images to improve the diagnostic accuracy. Reference [14] proposed a dictionary learning method combining with the geometrical structure by group sparse coding, but it did not talk about the time complexity of the algorithms based on sparse representation. To realize the medical image fusion based on SR with decision map, we add the local structure and energy information of source images into the decision map to improve the speed of the algorithm and the quality of the fused results. Using the maps to remain more structure and energy information in fused images will improve the quality of the results

The Framework of the Proposed Method

Experiments

Findings

Conclusion