Coupled Feature Learning for Multimodal Medical Image Fusion
This addresses the problem of improving diagnostic accuracy in medical imaging by providing a general fusion method for various modalities, though it appears incremental as it builds on existing sparse representation techniques.
The paper tackled multimodal medical image fusion by proposing a coupled dictionary learning method that avoids intensity attenuation and information loss, achieving competitive performance and execution times compared to state-of-the-art methods in experiments with MR-CT and MR-PET images.
Multimodal image fusion aims to combine relevant information from images acquired with different sensors. In medical imaging, fused images play an essential role in both standard and automated diagnosis. In this paper, we propose a novel multimodal image fusion method based on coupled dictionary learning. The proposed method is general and can be employed for different medical imaging modalities. Unlike many current medical fusion methods, the proposed approach does not suffer from intensity attenuation nor loss of critical information. Specifically, the images to be fused are decomposed into coupled and independent components estimated using sparse representations with identical supports and a Pearson correlation constraint, respectively. An alternating minimization algorithm is designed to solve the resulting optimization problem. The final fusion step uses the max-absolute-value rule. Experiments are conducted using various pairs of multimodal inputs, including real MR-CT and MR-PET images. The resulting performance and execution times show the competitiveness of the proposed method in comparison with state-of-the-art medical image fusion methods.