Multimodal Medical Image Fusion Techniques – A Review

(E-pub Ahead of Print)

Author(s): T. Tirupal*, B. Chandra Mohan, S. Srinivas Kumar

Journal Name: Current Signal Transduction Therapy

Become EABM
Become Reviewer
Call for Editor


The main objective of image fusion for multimodal medical images is to retrieve valuable information by combining multiple images obtained from various sources into a single image suitable for better diagnosis. In this paper, a detailed survey on various existing medical image fusion algorithms, with a comparative discussion is presented. Image fusion algorithms available in the current literature are categorized into various methods known as (1) morphological methods, (2) human value system operator based methods, (3) sub-band decomposition methods, (4) neural network based methods, and (5) fuzzy logic based methods. This research concludes that even though there exists a few open-ended creative and logical difficulties, the fusion of medical images in many combinations assists in utilizing medical image fusion for medicinal diagnostics and examination. There is tremendous progress in the fields of deep learning, artificial intelligence and bio-inspired optimization techniques. Effective utilization of these techniques can be used to further improve the efficiency of image fusion algorithms.

Keywords: Image fusion, Multimodal, Diagnosis, Morphological, Neural network, Fuzzy logic

open access plus

Rights & PermissionsPrintExport Cite as

Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1574362415666200226103116

Article Metrics

PDF: 5