Multimodal medical image fusion algorithm in the era of big data

TLDR

Multimodal medical imaging captures different modalities of a given organ, and robust fusion algorithms are needed to combine these complementary views for better diagnostic decisions. This study proposes a novel multimodal medical image fusion algorithm applicable to a wide range of diagnostic problems. The algorithm fuses images using a boundary‑measured pulse‑coupled neural network strategy and an energy attribute strategy within a non‑subsampled shearlet transform domain, and was validated on over 100 image pairs from glioma, Alzheimer’s, and metastatic bronchogenic carcinoma datasets. Qualitative and quantitative evaluations demonstrate that the proposed method outperforms most existing algorithms, offering valuable insights for medical diagnosis.

Abstract

Abstract In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured. Each of these images will represent a modality that will render the examined organ differently, leading to different observations of a given phenomenon (such as stroke). The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. It is based on the application of a boundary measured pulse-coupled neural network fusion strategy and an energy attribute fusion strategy in a non-subsampled shearlet transform domain. Our algorithm was validated in dataset with modalities of several diseases, namely glioma, Alzheimer’s, and metastatic bronchogenic carcinoma, which contain more than 100 image pairs. Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.