Fuzzy Clustering Segmentation Algorithm Research for Biomedical Image Based on Artificial Life

Abstract

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, the segmented partitions emerge when the state of the lives reaches an equilibrium. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. Then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the biomedical image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.