A novel clustering approach for the segmentation of pathological cells images

In this paper, a novel clustering method which combines the advantages of the density-based algorithm for discovering clusters in large spatial databases with noise (DBSCAN) and K-means is proposed. The proposed method can classify the pathological cell and the normal cell to two cluster memberships and the disturbances can also be eliminated from the image. In addition, by morphological image processing and Sobel edge algorithm, the pathological cell image can be segmented accurately from the image with pathological cell and normal cell. Finally, some images are illustrated to demonstrate the proposed method is superior to K-means and DBSCAN.