Fuzzy clustering-based methods and statistical models have been widely used for image segmentation. To improve segmentation accuracy

this study develops a novel robust spatial factor-based fuzzy clustering algorithm by introducing statistical information into the fuzzy objective function. A novel spatial factor is proposed to overcome the impact of noise on images. The proposed spatial factor is constructed based on the posterior and prior probabilities by incorporating the spatial information between neighboring pixels. It acts as a linear filter that smoothens and restores noise-corrupted images. The proposed spatial factor is fast

easy to implement

and capable of preserving details. The negative logarithm joint probability

which serves as a dissimilarity function

considers the prior probabilities and thus improves the capability to identify the class of each pixel. Integrating the dissimilarity function and novel spatial factor into the fuzzy objective function

we can obtain the final segmentations by iteratively minimizing the objective function. The comparison results on synthetic images demonstrate that the proposed algorithm can realize accurate segmentation and strong de-noising. The comparison results on color images demonstrate that the proposed algorithm can produce satisfactory segmentation results and accuracies by utilizing the suitable feature descriptor. The proposed algorithm address the drawbacks of current segmentation algorithms and further improve the accuracy for image segmentation. It outperforms state-of-the-art segmentation approaches in terms of accuracy. The proposed algorithm applies to image with noise and color image in aid of texture features.