Improvement of Fuzzy C-Mean Using Local Laplacian Filter for Image Segmentation

The FCM (Fuzzy C-Mean) is on of algorithms used in the background image separation research. This study aims to improve the quality of image segmentation using FCM algorithm with Local Laplacian Filter. Thus, we applied the Local Laplacian Filter into the V (Value) and S (Saturation) component of the image. The result of Laplacian Filter then clustered using FCM method. The result of segmentation using FCM and Local Laplacian Filter will be compared with ground-truths images to reveal the value of the PSNR (Peak Signal to Noise Ratio) and the MSE (Mean Square Error). This study also compared the results of the MSE and PSNR with the FCM and K-Mean algorithms. The image is done preprocessing first by using Local Laplacian Filter which gives the color contrast to the image. So the image becomes sharper and when the image changed to the color space HSV already looks quite striking color differences between objects with the background. The results of this study indicate that segmentation using FCM and Local Laplacian Filter has the best MSE and PSNR results compared to the 2 tested algorithms.