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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Medical image fusion using segment graph filter and sparse representation.

Qiaoqiao Li¹, Weilan Wang¹, Guoyue Chen²

¹Key Laboratory of China's Ethnic Languages and Information Technology of the Ministry of Education, Northwest Minzu University, Lanzhou, China.

Computers in Biology and Medicine

|February 7, 2021

Summary

This summary is machine-generated.

This study introduces a new medical image fusion method using segment graph filters (SGF) and sparse representation (SR). The approach enhances fused image quality by preserving edge information and integrating base and detail image fusion strategies.

Keywords:

Edge preserving Medical image fusion Segment graph filter Sparse representation

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Area of Science:

Medical Imaging
Image Processing
Computer Vision

Background:

Medical image fusion is crucial for enhancing diagnostic accuracy by combining complementary information from multiple sources.
Existing fusion methods often struggle to preserve fine details and structural information effectively.

Purpose of the Study:

To develop a novel medical image fusion approach that improves the preservation of edge information and overall image quality.
To leverage the strengths of segment graph filter (SGF) for image decomposition and sparse representation (SR) for detail fusion.

Main Methods:

Source images were decomposed into base and detail layers using the segment graph filter (SGF).
Base layers were fused using a normalized Shannon entropy-based rule.
Detail layers were fused using a sparse representation (SR)-based method.
The final fused image was reconstructed by combining the fused base and detail layers.

Main Results:

The proposed method effectively integrates edge information into the fused image.
Quantitative evaluation using five metrics (feature-based, structure-based, normalized mutual information, nonlinear correlation information entropy, and phase congruency) demonstrated robust performance.
Subjective visual assessment and objective quantification showed the fusion performance is comparable to state-of-the-art methods.

Conclusions:

The proposed SGF and SR-based medical image fusion method offers a significant improvement in preserving image details and structural information.
This approach provides a promising technique for enhancing medical image analysis and diagnostic capabilities.