Multimodal Medical Image Fusion Approach Using PCNN Model and Shearlet Transforms via Max Flat FIR Filter

Abstract

Pulse-coupled neural networks are a subpart of deep learning (DL) methodologies, which has vast number of applications. One of the preferred applications is multimodal medical image fusion, where different modality images such as MR scan images, CT scan images, and PET scan images are needed to be fused in one of the combinations to provide promising results to diagnose the abnormalities that were unable to detect in the individual images. When accomplishment a precise diagnosis or anatomy of the body, one type of medical imaging modality may not be adequate, imposing the fusion of images from several medical imaging modalities in order to deliver a final result in the immense popular of medical applications. In this work, MRI and PET scan images are fused by means of PCNN and shearlet transformation to yield better results.