Traditional Fuzzy C-means Algorithm Research Articles

New two-dimensional fuzzy C-means clustering algorithm for image segmentation

To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation.

A Novel Clustering Method for Quick Partial Volume Estimation in MR Brain Images

Automated brain MR image segmentation is a challenging pattern recognition problem that received significant attention lately. The most popular solutions involve fuzzy c-means (FCM) or similar clustering mechanisms. Several improvements have been made to the standard FCM algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. This paper presents a modified FCM-based method that targets accurate and fast segmentation in case of mixed noises. The proposed method extracts a scalar feature value from the neighbourhood of each pixel, using a context dependent filtering technique that deals with both spatial and grey level distances. These features are clustered afterwards by the histogram-based approach of the enhanced FCM algorithm. Results were evaluated based on synthetic phantoms and real MR images. Test experiments revealed that the proposed method provides better results compared to other reported FCM-based techniques. The time complexity of the proposed method is situated well below the traditional FCM algorithm. The achieved segmentation and the obtained fuzzy membership values represent excellent support for deformable contour model based cortical surface reconstruction methods.

A research on fast FCM algorithm based on weighted sample

To improve the computational performance of the fuzzy C-means (FCM) algorithm used in dataset clustering with large numbers, the concepts of the equivalent samples and the weighting samples based on eigenvalue distribution of the samples in the feature space were introduced and a novel fast cluster algorithm named weighted fuzzy C-means (WFCM) algorithm was put forward, which came from the traditional FCM algorithm. It was proved that the cluster results were equivalent in dataset with two different cluster algorithms: WFCM and FCM. Furthermore, the WFCM algorithm had better computational performance than the ordinary FCM algorithm. The experiment of the gray image segmentation showed that the WFCM algorithm is a fast and effective cluster algorithm.