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Related Experiment Video

Updated: Nov 27, 2025

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

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Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy.

Husein S Naji Alwerfali¹, Mohammed A A Al-Qaness², Mohamed Abd Elaziz³

¹School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

A new metaheuristic (MH) method, the modified spherical search optimizer (SSO) combined with the sine cosine algorithm (SCA), improves multi-level thresholding for image segmentation. This SSOSCA approach, using Fuzzy entropy, outperforms existing techniques on the Berkeley dataset.

Keywords:

fuzzy entropy image segmentation metaheuristics multi-level thresholding sine cosine algorithm (SCA)spherical search optimizer (SSO)

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Multi-level thresholding is crucial for image segmentation but struggles with optimal threshold selection.
Traditional methods often fail to determine suitable threshold values, necessitating advanced approaches.
Metaheuristic (MH) methods, inspired by natural swarm behaviors, offer a robust solution for optimization challenges in image segmentation.

Purpose of the Study:

To introduce an enhanced multi-level thresholding technique using a novel metaheuristic algorithm.
To improve the exploitation capabilities of the Spherical Search Optimizer (SSO) by integrating Sine Cosine Algorithm (SCA) operators.
To leverage Fuzzy entropy as a fitness function for evaluating segmentation quality.

Main Methods:

A modified Spherical Search Optimizer (SSO) algorithm was developed by incorporating Sine Cosine Algorithm (SCA) operators.
The proposed SSOSCA method was utilized for multi-level thresholding in image segmentation.
Fuzzy entropy was employed as the primary fitness function to assess the performance of the SSOSCA algorithm.
The method was evaluated using various images from the Berkeley dataset.

Main Results:

The proposed SSOSCA method demonstrated superior performance in image segmentation compared to existing techniques.
Quantitative evaluation using standard image segmentation measures confirmed the effectiveness of the SSOSCA approach.
The integration of SCA operators significantly enhanced the exploitation ability of the SSO, leading to better thresholding results.

Conclusions:

The SSOSCA method offers an effective and efficient solution for multi-level thresholding in image segmentation.
The study validates the utility of combining different metaheuristic strategies to overcome limitations in optimization tasks.
The proposed approach provides a promising alternative for various image analysis applications requiring precise segmentation.