2019/08/11 by Nishant Kumar, Nico Hoffmann, Kumar, Nishant +9
Engineering · Neuroscience · #Advanced Image Fusion Techniques #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Photoacoustic and Ultrasonic Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.03958
openalex publication_date 2019/08/11 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Multimodal medical image fusion helps in combining contrasting features from\ntwo or more input imaging modalities to represent fused information in a single\nimage. One of the pivotal clinical applications of medical image fusion is the\nmerging of anatomical and functional modalities for fast diagnosis of malignant\ntissues. In this paper, we present a novel end-to-end unsupervised\nlearning-based Convolutional Neural Network (CNN) for fusing the high and low\nfrequency components of MRI-PET grayscale image pairs, publicly available at\nADNI, by exploiting Structural Similarity Index (SSIM) as the loss function\nduring training. We then apply color coding for the visualization of the fused\nimage by quantifying the contribution of each input image in terms of the\npartial derivatives of the fused image. We find that our fusion and\nvisualization approach results in better visual perception of the fused image,\nwhile also comparing favorably to previous methods when applying various\nquantitative assessment metrics.\n