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Multi-level thresholding segmentation for pathological images: Optimal performance design of a new modified

Lili Ren¹, Dong Zhao², Xuehua Zhao³

¹High Performance Computing Center, Changchun Normal University, Changchun, Jilin, 130032, China.

Computers in Biology and Medicine

|August 12, 2022

Summary

A novel multi-level thresholding segmentation (MLTS) method using modified differential evolution (MDE) offers improved pathological image analysis. This approach enhances medical diagnostics by providing accurate segmentation and avoiding common algorithm pitfalls.

Keywords:

Breast cancer Meta-heuristic Multi-level thresholding segmentation Skin cancer Swarm-intelligence

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Area of Science:

Medical image analysis
Computational intelligence
Pathology

Background:

Accurate pathological image analysis is vital for advancing medical diagnostics.
Existing segmentation methods can suffer from stagnation and local optima.

Purpose of the Study:

To introduce a novel multi-level thresholding segmentation (MLTS) method.
To enhance pathological image segmentation using a new evolutionary algorithm, modified differential evolution (MDE).

Main Methods:

Development of a novel evolutionary algorithm, modified differential evolution (MDE), incorporating concepts from black holes, white holes, and wormholes.
Implementation of the MDE algorithm for multi-level thresholding segmentation (MLTS) of pathological images.
Validation of MDE using 30 benchmark functions and empirical comparison of the MLTS method against existing techniques on standard and medical images (breast and skin cancer).

Main Results:

The modified differential evolution (MDE) algorithm demonstrated significant convergence accuracy and an ability to escape local optima.
The proposed MDE-based MLTS method achieved high-quality segmentation results for pathological images.
Experimental validation confirmed the MDE-based MLTS approach's superior performance across various assessment indices compared to other methods.

Conclusions:

The developed modified differential evolution (MDE) algorithm effectively overcomes limitations of traditional evolutionary algorithms.
The MDE-based multi-level thresholding segmentation (MLTS) method provides an efficient and robust solution for pathological image analysis.
This approach holds significant potential for improving the accuracy and reliability of medical diagnostic tools.