Welcome to the InfoSci Platform
Reference Hub1
Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion

Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion

Vijayarajan Rajangam, Sangeetha N., Karthik R., Kethepalli Mallikarjuna
Copyright: © 2021 |Pages: 22
ISBN13: 9781799866909|ISBN10: 1799866904|EISBN13: 9781799866923
DOI: 10.4018/978-1-7998-6690-9.ch008
Cite Chapter Cite Chapter

MLA

Rajangam, Vijayarajan, et al. "Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion." Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, edited by Alex Noel Joseph Raj, et al., IGI Global, 2021, pp. 145-166. https://doi.org/10.4018/978-1-7998-6690-9.ch008

APA

Rajangam, V., N., S., R., K., & Mallikarjuna, K. (2021). Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion. In A. Raj, V. Mahesh, & R. Nersisson (Eds.), Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments (pp. 145-166). IGI Global. https://doi.org/10.4018/978-1-7998-6690-9.ch008

Chicago

Rajangam, Vijayarajan, et al. "Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion." In Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, edited by Alex Noel Joseph Raj, Vijayalakshmi G. V. Mahesh, and Ruban Nersisson, 145-166. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6690-9.ch008

Export Reference

Mendeley
Favorite

Abstract

Multimodal imaging systems assist medical practitioners in cost-effective diagnostic methods in clinical pathologies. Multimodal imaging of the same organ or the region of interest reveals complementing anatomical and functional details. Multimodal image fusion algorithms integrate complementary image details into a composite image that reduces clinician's time for effective diagnosis. Deep learning networks have their role in feature extraction for the fusion of multimodal images. This chapter analyzes the performance of a pre-trained VGG19 deep learning network that extracts features from the base and detail layers of the source images for constructing a weight map to fuse the source image details. Maximum and averaging fusion rules are adopted for base layer fusion. The performance of the fusion algorithm for multimodal medical image fusion is analyzed by peak signal to noise ratio, structural similarity index, fusion factor, and figure of merit. Performance analysis of the fusion algorithms is also carried out for the source images with the presence of impulse and Gaussian noise.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.