Peak-Graph-Based Fast Density Peak Clustering for Image Segmentation

Fuzzy c-means (FCM) algorithm as a traditional clustering algorithm for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor segmentation results with inconsistent regions. For the remedy, superpixel technologies are applied, but spatial information preservation highly relies on the quality of superpixels. Density peak clustering algorithm (DPC) can reconstruct spatial information of arbitrary-shaped clusters, but its high time complexity O(n(2)) and unrobust allocation strategy decrease its applicability for image segmentation. Herein, a fast density peak clustering method (PGDPC) based on the kNN distance matrix of data with time complexity O(nlog(n)) is proposed. By using the peak-graph-based allocation strategy, PGDPC is more robust in the reconstruction of spatial information of various complex-shaped clusters, so it can rapidly and accurately segment images into high-consistent segmentation regions. Experiments on synthetic datasets, real and Wireless Capsule Endoscopy (WCE) images demonstrate that PGDPC as a fast and robust clustering algorithm is applicable to image segmentation.

Automated summary

The traditional Fuzzy c-means algorithm lacks local spatial information preservation in image segmentation, while solutions like superpixel technologies and Density peak clustering algorithm have limitations. A fast density peak clustering method based on kNN distance matrix is proposed for more robust spatial information reconstruction and high-consistent image segmentation. Experiments show its applicability for image segmentation.