Contents
What is fuzzy C means clustering in image processing?
Fuzzy C-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. Fuzzy logic is a multi-valued logic derived from fuzzy set theory. FCM is popularly used for soft segmentations like brain tissue model.
How fuzzy concept is important in clustering?
In fuzzy clustering, each data point can have membership to multiple clusters. First, a new threshold value defining two clusters may be generated. Next, new membership coefficients for each data point are generated based on clusters centroids, as well as distance from each cluster centroid.
What is fuzzy segmentation?
Hence, each fuzzy image segmentation is characterized by means of a fuzzy set over the set of edges, which can be then understood as the fuzzy boundary of the image. Some computational experiences are included in order to show the obtained fuzzy boundaries of some digital images.
How fuzzy C-means clustering works?
This algorithm works by assigning membership to each data point corresponding to each cluster center on the basis of distance between the cluster center and the data point. More the data is near to the cluster center more is its membership towards the particular cluster center.
Why we use fuzzy C-means clustering?
Fuzzy c-means clustering has can be considered a better algorithm compared to the k-Means algorithm. Unlike the k-Means algorithm where the data points exclusively belong to one cluster, in the case of the fuzzy c-means algorithm, the data point can belong to more than one cluster with a likelihood.
What are the two approaches of image segmentation?
Following are the primary types of image segmentation techniques: Thresholding Segmentation. Edge-Based Segmentation. Region-Based Segmentation.
How is fuzzy c-means clustering used in segmentation?
Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide-spread prevalence, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation.
How is enfcm used in Fuzzy C clustering?
Szilgyi et al. [10] proposed the enhanced FCM (EnFCM) algorithm, in which an averaging filter is used to evaluate the neighbors and the FCM clustering is performed on the basis of gray level histograms to reduce the time cost.
Is the weighted image patch algorithm robust to noise?
However, in spite of its computational efficiency and wide-spread prevalence, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, we propose the weighted image patch-based FCM (WIPFCM) algorithm for image segmentation.
What kind of algorithm is used for Fuzzy C?
Kang et al. [14] proposed a spatial homogeneity-based FCM (SHFCM) algorithm, which makes use of the statistical information for every pixel in a local neighborhood. Wang et al. [15] incorporated both the local spatial context and non-local information into the dissimilarity measure, and proposed the LNLFCM algorithm.