Residual-driven Fuzzy C-Means Clustering for Image Segmentation
This work addresses image segmentation challenges in noisy environments, such as medical and real-world images, but is incremental as it builds on existing FCM frameworks.
The authors tackled the problem of poor image segmentation due to noise by developing a residual-driven Fuzzy C-Means (FCM) algorithm that accurately estimates the residual between observed and noise-free images, leading to superior effectiveness and efficiency over existing FCM-related methods.
Due to its inferior characteristics, an observed (noisy) image's direct use gives rise to poor segmentation results. Intuitively, using its noise-free image can favorably impact image segmentation. Hence, the accurate estimation of the residual between observed and noise-free images is an important task. To do so, we elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual estimation and leads noise-free image to participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related fidelity term derived from the distribution of different types of noise. Built on this framework, we present a weighted $\ell_{2}$-norm fidelity term by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise. Besides, with the constraint of spatial information, the residual estimation becomes more reliable than that only considering an observed image itself. Supporting experiments on synthetic, medical, and real-world images are conducted. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over existing FCM-related algorithms.