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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 12

Image segmentation method for adaptive region growing coupled neural p systems

Xu Jiachang
Ye Xuxiu
School of Computer Science & Engineering, Anhui University of Science & Technology, Huainan 232001, China
DOI: 10.19734/j.issn.1001-3695.2025.06.0163

Abstract

To address the issue that the coupled neural P system relies on the selection of initial seed points when using the spiking mechanism for region growing. This paper proposes an image segmentation method based on adaptive region growing coupled neural P systems (ARGCNP) . This method employs the global search capability of the golden jackal optimization (GJO) algorithm and introduces four strategies to enhance the global optimization performance of GJO. The aim is to identify the optimal threshold points in the image to optimize the selection of seed points in region growing. The experiments first evaluate the performance of the improved GJO on the CEC2017 benchmark functions, and show that it achieves the best overall performance. The proposed method then applies ARGCNP to the segmentation of both color and medical images, and quantitatively assesses the segmentation performance using three evaluation metrics, including peak signal-to-noise ratio. The results demonstrate that the proposed method improves segmentation accuracy and enhances the stability of the results, confirming that ARGCNP offers clear advantages in practical applications and effectively meets the requirements of image segmentation tasks.

Foundation Support

安徽理工大学医学专项培育项目(YZ2023H2B008)
南方林业与生态应用技术国家工程实验室开放基金项目(2023NFLY08)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.06.0163
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 12

Publish History

[2025-08-21] Accepted Paper

Cite This Article

许家昌, 叶栩秀. 自适应区域生长耦合神经P系统的图像分割方法 [J]. 计算机应用研究, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0163. (Xu Jiachang, Ye Xuxiu. Image segmentation method for adaptive region growing coupled neural p systems [J]. Application Research of Computers, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0163. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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