Kernel possibilistic fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering

A lot of research has been conducted on many variants of the Fuzzy c-means (FCM) clustering algorithm incorporating local spatial neighborhood information to improve segmentation accuracy and robustness to noise. Among these variants, a fast and robust FCM (FRFCM) clustering algorithm performs fast and robustly to noise for both grayscale and color images. Though, FRFCM is fast but segmentation performance needs improvement. This work presents an improved variant of the FRFCM algorithm, based on the kernel metric and possibilistic fuzzy c-means approach. The proposed method named Kernel Possibilistic Fast-Robust Fuzzy c-means (KPFRFCM) algorithm overcomes the disadvantages of FRFCM i.e. the poor segmentation performance and less robustness to noise, for both grayscale and color images. Experiments performed on various types of images without noise and images degraded by different types of noises with different degrees, prove that proposed KPFRFCM is more efficient and more robust to noise when compared with existing state-of-the-art algorithms for image segmentation.