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

Hind R.M,Ali Abdulkarem,Farah Abbas

doi:10.14569/ijacsa.2015.061224

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.

Highlights

Segmentation plays an integral part in partitioning an image into sub-regions on a particular application
Image segmentation methods are of three categories: edge based methods, region based methods, and pixel based methods .K-Means clustering is technical way in pixel-based methods [2]
Peak signal-to-noise ratio, often-abbreviated PSNR, is an engineering term for the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation

Summary

INTRODUCTION

Segmentation plays an integral part in partitioning an image into sub-regions on a particular application. The traditional methods used for the medical image segmentation are Clustering, threshold, region based Segmentation, edge based methods and ANN Image Segmentation [1]. Image segmentation methods are of three categories: edge based methods, region based methods, and pixel based methods .K-Means clustering is technical way in pixel-based methods [2]. While K-Means discovers compound clusters (a point belong to only one cluster), Fuzzy C-Means is a more statistically formalized method and finds out soft clusters where a particular point can belong to more than one block with certain probability[3]. The clustering technique used for image segmentation. To achieve the super pixel information, many clustering techniques can be classified. The purpose of using clustering technique is to get the proper result with high-efficiency, which has an effect on storage image [4].

K-MEANS AND FUZZY C-MEANS

EXPERIMENTAL RESULTS

CONCLUSION