Image Segmentation Using PSO-Enhanced K-Means Clustering and Region Growing Algorithms

Authors

  • Nassir H. Salman Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq
  • Suhaila N. Mohammed Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq http://orcid.org/0000-0002-9530-2487

DOI:

https://doi.org/10.24996/ijs.2021.62.12.35

Keywords:

Image segmentation, K-Means, Particle Swarm Intelligence (PSO), Li‘s method, Region growing

Abstract

    Image segmentation is a basic image processing technique that is primarily used for finding segments that form the entire image. These segments can be then utilized in discriminative feature extraction, image retrieval, and pattern recognition. Clustering and region growing techniques are the commonly used image segmentation methods. K-Means is a heavily used clustering technique due to its simplicity and low computational cost. However, K-Means results depend on the initial centres’ values which are selected randomly, which leads to inconsistency in the image segmentation results. In addition, the quality of the isolated regions depends on the homogeneity of the resulted segments. In this paper, an improved K-Means clustering algorithm is proposed for image segmentation. The presented method uses Particle Swarm Intelligence (PSO) for determining the initial centres based on Li’s method. These initial centroids are then fed to the K-Means algorithm to assign each pixel into the appropriate cluster. The segmented image is then given to a region growing algorithm for regions isolation and edge map generation. The experimental results show that the proposed method gives high quality segments in a short processing time.

Downloads

Download data is not yet available.

Downloads

Published

2021-12-30

How to Cite

Salman, N. H. ., & Mohammed, S. N. . (2021). Image Segmentation Using PSO-Enhanced K-Means Clustering and Region Growing Algorithms. Iraqi Journal of Science, 62(12), 4988–4998. https://doi.org/10.24996/ijs.2021.62.12.35

Issue

Section

Computer Science