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Image Multi-Threshold Segmentation Based on Variable Precision Rough Set and K-L Roughness Particle Swarm Optimization

Year: 2025

Type: article

Abstract: This paper proposes an image multi-threshold segmentation algorithm based on variable precision rough sets and K-L roughness particle swarm optimization. The algorithm does not require a priori knowledge outside the image and employs variable precision rough sets to address the uncertainty problem in image segmentation. The optimal segmentation threshold is obtained by combining K-L divergence and roughness, and an improved particle swarm optimization algorithm is used to enhance segmentation efficiency. Experimental results demonstrate that the proposed algorithm effectively solves the uncertainty problem in segmentation and achieves better segmentation performance compared to other algorithms.

Source: Tehnicki vjesnik - Technical Gazette

DOI: 10.17559/tv-20240301001359

Authors: She, Zhiyong, Song, Tao, Zhang, Dongpo, Feng, Yueping

Institutions: Xinjiang University of Political Science and Law, Geely University of China, Jilin University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $431

OpenAlex ID: W4408046676

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