Medical CT Edge Detection Algorithm Based on Improved Fuzzy Clustering Analysis

An effective PSO fuzzy clustering edge detection algorithm is proposed. PSO algorithm and Fuzzy C-Mean algorithm are combined to overcome two shortcomings, namely the initialization sensitivity and the local minimum of standard FCM algorithm in image edge detection. At first, a vector is constructed to describe edge point information, which includes neighborhood homogeneity information measure, orientation information measure, and gradient strength. Then we regard a pixel point in a gray image as a data sample, and its gray values which are worked by our defined vector operator as the feature vectors of this data sample, in this way we can obtain a data set with three-dimensional features. Then we use the PSO fuzzy clustering algorithm on this data set, it can detect out the edge points adaptively. Simulated experiment shows the algorithm can effectively reduce the noises of the image, and its results needn't to be adjusted, which can enhance the precision of edge orientation.