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An efficient multi-level thresholding method for breast thermograms analysis based on an improved BWO algorithm.

Simrandeep Singh¹, Harbinder Singh², Nitin Mittal³

¹Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India.

BMC Medical Imaging

|July 30, 2024

Summary

This summary is machine-generated.

This study introduces an improved Black Widow Optimization Algorithm (IBWOA) for enhanced breast thermography image segmentation. IBWOA offers superior accuracy and efficiency in early breast cancer detection compared to other optimization methods.

Keywords:

Breast cancer IBWOA Kapur’s entropy Otsu Thermography Thresholding

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Area of Science:

Medical Imaging
Computational Intelligence
Biomedical Engineering

Background:

Breast cancer is a leading global health concern, necessitating advanced early detection methods.
Thermography presents a promising, non-ionizing alternative for breast cancer screening.
Accurate medical image segmentation is vital for effective analysis and diagnosis.

Purpose of the Study:

To develop and evaluate an improved Black Widow Optimization Algorithm (IBWOA) for segmenting breast thermography images.
To enhance the exploration and exploitation capabilities of the standard Black Widow Optimization Algorithm (BWOA).
To validate the superiority of IBWOA against other optimization algorithms in thermographic image segmentation.

Main Methods:

Implementation of IBWOA incorporating Levy flights for enhanced exploration and quasi-opposition-based learning for improved exploitation.
Segmentation of thermographic images using IBWOA and comparison with established methods (Otsu, Kapur's entropy).
Performance evaluation against Harris Hawks Optimization (HHO), LSHADE, WOA, SCA, and BWO using quantitative metrics.

Main Results:

IBWOA demonstrated superior qualitative and quantitative performance in segmenting breast thermography images.
Key performance metrics including fitness value, PSNR, SSIM, and FSIM showed significant improvements with IBWOA.
The proposed algorithm effectively addressed the stagnation and exploration-exploitation balance issues of the standard BWOA.

Conclusions:

The improved Black Widow Optimization Algorithm (IBWOA) is a highly effective and superior method for breast thermography image segmentation.
IBWOA offers enhanced accuracy and reliability for early breast cancer detection through improved image analysis.
This research validates the potential of IBWOA in advancing medical imaging diagnostics.