Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier

This paper presents Evaluation K-mean and Fuzzy c-mean image segmentation based Clustering classifier. It was followed by thresholding and level set segmentation stages to provide accurate region segment. The proposed stay can get the benefits of the K-means clustering. The performance and evaluation of the given image segmentation approach were evaluated by comparing K-mean and Fuzzy c-mean algorithms in case of accuracy, processing time, Clustering classifier, and Features and accurate performance results. The database consists of 40 images executed by K-mean and Fuzzy c-mean image segmentation based Clustering classifier. The experimental results confirm the effectiveness of the proposed Fuzzy c-mean image segmentation based Clustering classifier. The statistical significance Measures of mean values of Peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) and discrepancy are used for Performance Evaluation of K-mean and Fuzzy c-mean image segmentation. The algorithm’s higher accuracy can be found by the increasing number of classified clusters and with Fuzzy c-mean image segmentation.

@article{Shaaban2015,
  title     = {Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {12},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Hind R.M Shaaban and Farah Abbas Obaid and Ali Abdulkarem Habib},
  doi       = {10.14569/IJACSA.2015.061224},
  url       = {https://doi.org/10.14569/IJACSA.2015.061224}
}