Research progress of multimodal medical image fusion methods
10.13491/j.issn.1004-714X.2023.05.020
- VernacularTitle:多模态医学图像融合方法的研究进展
- Author:
Wei CHEN
1
,
2
,
3
,
4
;
Kangkang SUN
1
,
2
,
3
,
4
;
Qixuan LI
2
,
3
,
4
;
Kai XIE
2
,
3
,
4
;
Xinye NI
2
,
3
,
4
Author Information
1. School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164 China
2. Department of Radiotherapy the Second People's Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003 China
3. Central Laboratory of Medical Physics, Nanjing Medical University, Changzhou 213003 China
4. Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003 China.
- Publication Type:ReviewArticles
- Keywords:
Multimodal;
Image fusion;
Deep learning;
Clinical diagnosis
- From:
Chinese Journal of Radiological Health
2023;32(5):580-585
- CountryChina
- Language:Chinese
-
Abstract:
In the current clinical diagnosis, medical images have become an important basis for diagnosis, and different modes of medical images provide different tissue information and functional information. Single-mode images can only provide single diagnostic information, by which difficult and complicated diseases cannot be diagnosed, and comprehensive and accurate diagnostic results can be obtained only with the help of multiple diagnostic information. The multimodal fusion technology fuses multiple modes of medical images into single-mode images, and thus the single-mode images contain complementary information between multiple modes of images, so that sufficient information for clinical diagnosis can be obtained in a single image. In this paper, the multimodal medical image fusion methods are sorted into two types, namely the traditional fusion method and the fusion method based on deep learning.
