Inspired GWO-based Multilevel Thresholding for Color Images Segmentation via M. Masi Entropy

Authors

  • I Made Satria Bimantara Program Studi Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana https://orcid.org/0000-0003-0924-6552
  • I Wayan Supriana Program Studi Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana
  • I Komang Arya Ganda Wiguna Program Studi Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana
  • Ida Bagus Gede Sarasvananda Program Studi Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Keywords:

Inspired Grey Wolf Optimizer, Multilevel Thresholding Segmentasi Citra Berwarna, Metode Otsu, Kapur Entropy, M. Masi Entropy

Abstract

Image segmentation is crucial in image processing and computer vision, with multilevel thresholding (ML-ISP) offering robust solutions for complex images. However, effectively applying ML-ISP to RGB color images remains a challenge due to computational complexity and the limitations of traditional optimization algorithms, such as the Grey Wolf Optimizer (GWO). This study proposes an Inspired Grey Wolf Optimizer (IGWO) to address these issues and enhance ML-ISP for RGB color images. The performance stability of IGWO is comprehensively evaluated using three distinct objective functions: the Otsu method, the Kapur Entropy, and the M. Masi Entropy. Qualitative and quantitative analyses using PSNR, SSIM, and UQI were conducted on benchmark images. Results consistently demonstrate that IGWO, particularly with M. Masi Entropy, achieves superior segmentation quality. This research incorporates GridSearch-based hyperparameter tuning. The findings highlight the effectiveness and robustness of the proposed IGWO approach for complex ML-ISP tasks on color images.

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Published

2025-10-01

Issue

Vol. 16 No. 2 (2025): Jurnal Buana Informatika, Volume 16, Nomor 02, Oktober 2025

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Articles

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