MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules

Abstract

Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to evaluate the fusion result. In visual evaluation the opinion of two radiologists was used and in quantitative evaluation the proposed fusion method was compared with six existing methods and used criteria were entropy, mutual information, discrepancy and overall performance.

• Image fusion
• fusion rules
• multiscale geometric analysis
• nonsubsampled contourlet transform

Acknowledgements

The authors would like to thank the anonymous reviewers for their invaluable suggestions. We are very thankful of Professor Bijan Bijan and Amir Arsalan Zamani, MD, who gave us their time. This article is extracted from a biomedical engineering master’s thesis, sponsored by the Biomedical Engineering Department, Science and Research Branch, Islamic Azad University Tehran, Iran.