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

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_S1 and KFCM_S2, were proposed to solve those drawbacks of BCFCM. However, KFCM_S1 and KFCM_S2 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_S1 and KFCM_S2 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_S1 and KFCM_S2. Experimental results show that the proposed GKFCM has better performance.