Segmentation of cervical cell images using mean-shift filtering and morphological operators

Screening plays an important role within the fight against cervical cancer. One of the most challenging parts in order to automate the screening process is the segmentation of nuclei in the cervical cell images, as the difficulty for performing this segmentation accurately varies widely within the nuclei. We present an algorithm to perform this task. After background determination in an overview image, and interactive identification of regions of interest (ROIs) at lower magnification levels, ROIs are extracted and processed at the full magnification level of 40x. Subsequent to initial background removal, the image regions are smoothed by mean-shift and median filtering. Then, segmentations are generated by an adaptive threshold. The connected components in the resulting segmentations are filtered with morphological operators by characteristics such as shape, size and roundness. The algorithm was tested on a set of 50 images and was found to outperform other methods.