Multimodal medical image fusion using multi-resolution transform

Abstract

Multimodal image fusion is used to enhance the quality of fused image in medical diagnosis by combining two images 'same scene' from different techniques, such as Computed Tomography (CT) scan or Magnetic Resonance Imaging (MRI) with Positron Emission Tomography (PET). In this paper, we present a hybrid technique using non-subsampled contourlet for contrast-enhancement to balance the requirement of local and global contrast enhancements of each input image appearance and biorthogonal CDF9/7 wavelet based on lifting scheme. Then the low frequency sub-bands are combined by a maximal selection rule and the high frequency sub-bands are combined by a maximal spatial frequency. In this technique, the proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details and unwanted image degradations etc. Subjective and objective evaluations show better performance of this new approach compared to existing techniques. This shows that the quality of fused image was better compared with existing methods in case of hybrid method.