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Related Experiment Video

Updated: Aug 28, 2025

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

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An adaptive multilevel thresholding method with chaotically-enhanced Rao algorithm.

Yagmur Olmez¹, Abdulkadir Sengur², Gonca Ozmen Koca¹

¹Department of Mechatronics Engineering, Faculty of Technology, University of Firat, 23119 Elazig, Turkey.

Multimedia Tools and Applications

|September 15, 2022

Summary

This summary is machine-generated.

This study introduces Chaotic Enhanced Rao (CER) algorithms for multilevel image thresholding, improving segmentation speed and accuracy. The novel approach automates threshold determination, outperforming existing methods on the BSDS300 dataset.

Keywords:

Chaotic search Image segmentation Metaheuristic methods Multilevel thresholding Rao algorithm

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Area of Science:

Computer Vision
Image Processing
Optimization Algorithms

Background:

Multilevel image thresholding is crucial for image segmentation.
Existing metaheuristic methods are complex and slow.
Manual threshold determination limits practical application.

Purpose of the Study:

To develop a simplified multilevel image thresholding approach using a novel optimization technique.
To introduce Chaotic Enhanced Rao (CER) algorithms with automatic threshold number determination.
To evaluate CER algorithm performance against established methods.

Main Methods:

Development of Chaotic Enhanced Rao (CER) algorithms utilizing eight chaotic maps (Logistic, Sine, Sinusoidal, Gauss, Circle, Chebyshev, Singer, Tent).
Automatic determination of the number of thresholds.
Performance evaluation using statistical metrics (BDE, PRI, VOI, GCE, SSIM, FSIM, RMSE, PSNR, NK, AD, SC, MD, NAE) on the BSDS300 dataset.

Main Results:

The proposed CER algorithm demonstrated superior performance in image segmentation based on PRI, SSIM, FSIM, PSNR, RMSE, AD, and NAE metrics.
Experimental results confirmed the effectiveness of CER algorithms on the BSDS300 dataset.
The method achieved better convergence speed and accuracy compared to existing approaches.

Conclusions:

The developed CER algorithms offer an efficient and accurate solution for multilevel image thresholding.
Automatic threshold number determination simplifies the segmentation process.
The proposed method represents a significant advancement over traditional metaheuristic techniques.