Aim: In this study, image segmentation is one of the fundamental aspects involved in image processing, which generally consists of discriminating objects of interest from its background. The RGB test image images use Kapur strategy for enhancement of entropy and to increase peak signal to noise ratio.The expansion of RGB test image is red,green and blue respectively. Materials and Methods: Two sets of 10 images were enhanced using kapur strategy and the PSNR were calculated. The PSNR data were further analyzed using k-means and threshold algorithm. The significant value of the observed data was analyzed using SPSS. Results: The PSNR values of the images were obtained from the Kapur strategy. The PSNR values are further processed in Threshold and K means algorithm. The mean values of the K means and Threshold algorithm are compared for the significant difference. The statistical results showed that means of threshold are greater than k-means. This shows that Threshold was found to be efficient in enhancement of RGB images. The comparison of k-means and threshold algorithm of significant value of (p<0.05, p=0.04). Conclusion: In this study, it was observed that the mean PSNR of RGB images processed using threshold clustering (9.6330) was greater than the mean k-means clustering algorithm(8.4460).