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Tensor Methods In Biomedical Image Analysis.

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Tensor Methods In Biomedical Image Analysis.

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Tensor Methods in Biomedical Image Analysis.

Farnaz Sedighin¹

¹Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Journal of Medical Signals and Sensors

|August 5, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Tensors are superior to matrices for analyzing complex biomedical data. This review highlights tensor-based methods for enhancing biomedical image analysis and future research directions.

Keywords:

Biomedical image enhancement tensor decomposition tensor networks

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Published on: November 27, 2017

Hybrid µCT-FMT imaging and image analysis

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Area of Science:

Multimodal data analysis
Biomedical signal and image processing

Background:

Matrices struggle with multidimensional and multimodal datasets, limiting analysis.
Tensors effectively capture higher-order correlations in complex data.
Biomedical data analysis requires accurate information extraction for patient health.

Purpose of the Study:

To comprehensively review tensor-based methods in biomedical image analysis.
To classify existing tensor methods and their applications.
To demonstrate the importance of tensors in biomedical image enhancement.

Main Methods:

Review of tensor-based approaches for signal and image processing.
Classification of tensor methods applied to biomedical datasets.
Analysis of simultaneous data exploitation using tensors (e.g., EEG and fMRI).

Main Results:

Tensor-based methods show promising performance in analyzing multidimensional biomedical data.
Tensors enable effective simultaneous analysis of multiple datasets from a single patient.
Identified the significance of tensors in improving biomedical image quality.

Conclusions:

Tensors offer a powerful framework for advanced biomedical image analysis.
This review provides a foundation for future research in tensor-based biomedical applications.
Tensor methods are crucial for extracting critical information from complex biomedical datasets.