Multimodal medical image fusion based on IHS and PCA

TLDR

Multimodal brain imaging fusion is crucial for combining PET’s functional information with MRI’s high‑resolution anatomical detail, yet existing methods often suffer from limited applicability and spatial or color distortion. This study aims to enhance fused image quality by integrating the complementary strengths of intensity‑hue‑saturation (IHS) and principal component analysis (PCA) techniques. The proposed algorithm merges IHS and PCA fusion processes to produce a single fused image that preserves spatial features and functional information without color distortion. Visual and quantitative evaluations demonstrate that the integrated IHS–PCA method outperforms conventional PCA, Brovey, and discrete wavelet transform approaches in fusion quality.

Abstract

The fusion of multimodal brain imaging for a given clinical application is a very important performance. Generally, the PET image indicates the brain function and has a low spatial resolution, the MRI image shows the brain tissue anatomy and contains no functional information. Hence, a perfect fused image should contain both more functional information and more spatial characteristics with no spatial and color distortion. There have been a number of approaches proposed for fusing multitask or multimodal image information. But, every approach has its limited domain for a particular application. Study indicated that intensity-hue-saturation (IHS) transform and principal component analysis (PCA) can preserve more spatial feature and more required functional information with no color distortion. The presented algorithm integrates the advantages of both IHS and PCA fusion methods to improve the fused image quality. Visual and quantitative analysis show that the proposed algorithm significantly improves the fusion quality; compared to fusion methods including PCA, Brovey, discrete wavelet transform (DWT).