# Enhanced MRI-PET fusion using Laplacian pyramid and empirical mode decomposition for improved oncology imaging

## TL;DR

This paper introduces a new MRI-PET image fusion method using Laplacian pyramid and empirical mode decomposition to improve oncology imaging quality.

## Contribution

A novel fusion method combining Laplacian pyramid and empirical mode decomposition for enhanced MRI-PET imaging.

## Key findings

- The proposed method outperforms existing techniques in visualization and objective metrics.
- It preserves structural details and reduces edge detail loss using a rolling guidance filter.

## Abstract

In the field of oncology imaging, the fusion of magnetic resonance imaging (MRI) and positron emission tomography (PET) modalities is crucial for enhancing diagnostic capabilities. This article introduces a novel fusion method that leverages the strengths of both modalities to overcome limitations associated with functional information in MRI and the spatial resolution in PET scans. Our approach integrates the Laplacian pyramid for extracting high and low-frequency components, along with empirical mode decomposition and phase congruency to preserve crucial structural details in the fused image. Additionally, a rolling guidance filter is employed to mitigate edge detail loss. Through extensive comparative experiments on multi-focus and multi-modal image datasets, our method consistently outperforms existing techniques in terms of visualization, objective metrics, and computational efficiency. The proposed fusion method demonstrates superior performance, establishing it as a compelling alternative for oncology imaging applications.