Structural Similarity based Anatomical and Functional Brain Imaging Fusion
This addresses the problem of fast diagnosis of malignant tissues for clinicians by fusing anatomical and functional brain images, though it is incremental as it builds on existing fusion techniques with a novel loss function.
The paper tackles the fusion of MRI-PET brain images for medical diagnosis by proposing an unsupervised CNN that uses Structural Similarity Index as a loss function, resulting in improved visual perception and favorable quantitative metrics compared to previous methods.
Multimodal medical image fusion helps in combining contrasting features from two or more input imaging modalities to represent fused information in a single image. One of the pivotal clinical applications of medical image fusion is the merging of anatomical and functional modalities for fast diagnosis of malignant tissues. In this paper, we present a novel end-to-end unsupervised learning-based Convolutional Neural Network (CNN) for fusing the high and low frequency components of MRI-PET grayscale image pairs, publicly available at ADNI, by exploiting Structural Similarity Index (SSIM) as the loss function during training. We then apply color coding for the visualization of the fused image by quantifying the contribution of each input image in terms of the partial derivatives of the fused image. We find that our fusion and visualization approach results in better visual perception of the fused image, while also comparing favorably to previous methods when applying various quantitative assessment metrics.