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Digital Object Identifier (DOI) : 10.14569/IJACSA.2015.061224
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 12, 2015.
Abstract: 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.
Hind R.M Shaaban, Farah Abbas Obaid and Ali Abdulkarem Habib, “Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier” International Journal of Advanced Computer Science and Applications(IJACSA), 6(12), 2015. http://dx.doi.org/10.14569/IJACSA.2015.061224