Medical image fusion using Transfer Learning and L-BFGS optimization algorithm

Jionghui Jiang

doi:10.1002/ima.22560

Medical image fusion using Transfer Learning and L-BFGS optimization algorithm

2021-02-23

3

PRE

AI

DOI：10.1002/ima.22560

Original

Original request for help

Save

Abstract

En 中文

Medical image fusion can effectively combine different images with different parts and complements each other, which is a hotspot in image processing. However, the medical image acquisition equipment usually obtains single image, so it is unable to use dataset to train convolutional neural network to extract stable image features. This paper proposes an approach using the Transfer Learning combined with the Limited Broyden, Fletcher, Goldfarb, and Shanno (L-BFGS) optimization algorithm to achieve single medical image fusion. The Transfer Learning which includes pre-trained model and parameters can be used as a feature extractor to extract or compress features in medical images. Firstly, different single medical images were input into the trained VGG16 Transfer Learning parameter model to extract image features, and then the maximum features between different images were obtained through feature comparison. Finally, L-BFGS optimization algorithm is used to approximate the initial image features to the maximum features, to achieve the fusion effect (the initial image can be any image of the same size and depth as the source image). The experimental results show that the algorithm is effective and very suitable for medical image fusion.

Keywords:

medical image fusion

Transfer Learning

L‐ BFGS

Medical image fusion using Transfer Learning and L-BFGS optimization algorithm

Abstract

AI Summary

Journal

Organization