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Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain.

Nahed Tawfik¹, Heba A Elnemr², Mahmoud Fakhr³

¹Computers and Systems Department, Electronics Research Institute, Joseph Tito St, El Nozha, Huckstep Cairo, Egypt. nahedtawfik@eri.sci.eg.

Journal of Digital Imaging

|June 29, 2022

Summary

This summary is machine-generated.

This study introduces a novel medical image fusion technique using stacked sparse auto-encoders (SSAE) and non-subsampled contourlet transform (NSCT). The method effectively merges multimodal medical images while preserving crucial details.

Keywords:

Deep Learning Image fusion Medical image modalities Non-subsampled contourlet transform (NSCT)Stacked sparse auto-encoder (SSAE)

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Area of Science:

Medical Imaging
Deep Learning
Image Processing

Background:

Medical image fusion combines information from multiple imaging modalities.
Deep learning methods show great efficiency and capability in feature extraction for image fusion.
Stacked sparse auto-encoder (SSAE) offers unsupervised feature extraction and complex data representation.

Purpose of the Study:

To develop and evaluate a novel medical image fusion method using SSAE.
To leverage SSAE's feature extraction for enhanced fusion of multimodal medical images.
To preserve essential details during the fusion process.

Main Methods:

Source images decomposed into low- and high-frequency sub-bands using non-subsampled contourlet transform (NSCT).
SSAE used for unsupervised feature extraction from high-frequency coefficients.
Spatial frequencies computed for high-frequency coefficient fusion.
Maximum-based fusion rule applied to low-frequency coefficients.
Inverse NSCT applied to reconstruct the fused image.

Main Results:

The proposed method effectively merges multimodal medical images.
Essential detail information is preserved in the fused images.
Experimental results demonstrate the method's efficacy across various medical image modalities.

Conclusions:

The developed SSAE-based medical image fusion method is effective.
The technique successfully integrates information from different medical imaging modalities.
Preservation of fine details is a key strength of this fusion approach.