A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction

Abstract

Bias-corrected fuzzy c-means (BCFCM) algorithm with spatial information is especially effective in image segmentation. Since it is computationally time taking and lacks enough robustness to noise and outliers, some kernel versions of FCM with spatial constraints, such as KFCM_S 1 and KFCM_S 2, were proposed to solve those drawbacks of BCFCM. However, KFCM_S 1 and KFCM_S 2 are heavily affected by their parameters. In this paper, we present a Gaussian kernel-based fuzzy c-means algorithm (GKFCM) with a spatial bias correction. The proposed GKFCM algorithm becomes a generalized type of FCM, BCFCM, KFCM_S 1 and KFCM_S 2 algorithms and presents with more efficiency and robustness. Some numerical and image experiments are performed to assess the performance of GKFCM in comparison with FCM, BCFCM, KFCM_S 1 and KFCM_S 2. Experimental results show that the proposed GKFCM has better performance.