Clustered nuclei splitting via curvature information and gray-scale distance transform

Clusters or clumps of cells or nuclei are frequently observed in two dimensional images of thick tissue sections. Correct and accurate segmentation of overlapping cells and nuclei is important for many biological and biomedical applications. Many existing algorithms split clumps through the binarization of the input images; therefore, the intensity information of the original image is lost during this process. In this paper, we present a curvature information, gray scale distance transform, and shortest path splitting line-based algorithm which can make full use of the concavity and image intensity information to find out markers, each of which represents an individual object, and detect accurate splitting lines between objects using shortest path and junction adjustment. The proposed algorithm is tested on both synthetic and real nuclei images. Experiment results show that the performance of the proposed method is better than that of marker-controlled watershed method and ellipse fitting method.