A novel method of cervical cell image segmentation via region merging and SLIC

Considering that the existing methods of cell segmentation are sensitive to noise, in this paper, a novel method was proposed to obtain the contour of cervical cell accurately and robustly. Firstly, the mean shift algorithm was utilized to smooth the cell image. Then, the initial contour of cervical cell was extracted by an adaptive threshold algorithm. Secondly, SLIC (Simple Linear Iterative Clustering, SLIC) was applied to the smoothed cell image to get the superpixels of the whole cell image. Finally, based on the initial contour, we can automatically set some marks for background and foreground in a cell image full of superpixels. Then the superpixels were merged by the rule of maximal similarity. A key property of our superpixel merging is that it does not require a preset threshold, and the non-marker background regions are merged with the marked area automatically, while the non-marker superpixels are identified to avoid from being merged into background. We validate our method via the cervical cell image database and demonstrate that our method can extract the contour of cytoplasm from a single-cell cervical smear image accurately in a relatively short time.