Multi-layer, multi-modal medical image intelligent fusion

Recently, deep learning has high popularity in the field of image processing due to its unique feature extraction property. This paper, proposes a novel multi-layer, multi-tier system called Multi-Layer Intelligent Image Fusion(MLIIF) with deep learning(DL) networks for visually enhanced medical ima...

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Bibliographic Details
Published in:Multimedia tools and applications 2022-12, Vol.81 (29), p.42821-42847
Main Authors: Nair, Rekha R., Singh, Tripty, Basavapattana, Abhinandan, Pawar, Manasa M.
Format: Article
Language:English
Subjects:
1221: Deep Learning for Image/Video Compression and Visual Quality Assessment
Computer Communication Networks
Computer Science
Computer vision
Data Structures and Information Theory
Deep learning
Feature extraction
Image enhancement
Image processing
Machine learning
Medical imaging
Multilayers
Multimedia Information Systems
Networks
Special Purpose and Application-Based Systems
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Summary:Recently, deep learning has high popularity in the field of image processing due to its unique feature extraction property. This paper, proposes a novel multi-layer, multi-tier system called Multi-Layer Intelligent Image Fusion(MLIIF) with deep learning(DL) networks for visually enhanced medical images through fusion. Implemented deep feature based multilayer fusion strategy for both high frequency and low frequency components to obtain more informative fused image from the source image sets. The hybrid MLIIF consists of VGG-19, VGG-11, and Squeezenet DL networks for different layer deep feature extraction from approximation and detailed frequency components of the source images. The robustness of the proposed multi-layer, multi-tier fusion system is validated by subjective and objective analysis. The effectiveness of the proposed MLIIF system is evaluated by error image calculation with the ground truth image and thus accuracy of the system. The source images utilized for the experimentations are collected from the website www.med.harvard.edu and the proposed MLIIF system obtained an accuracy of 95%. The experimental findings indicate that the proposed system outperforms compared with existing DL networks.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-13482-y