Cell nuclei segmentation is a key issue in automatic cell image analysis for nuclear malignancy. However, due to the
complexity of microscopic images, it is usually not easy to obtain satisfied segmentation results, especially on the
separation of touching or overlapping nuclei. We propose a method to separate overlapping nuclei whose shapes are
similar to ellipses, even if they are tightly clustered and no edge is present where they touch. As a class-specific
approach, it introduces a statistical shape model as an extra constraint within the energy functional that measures the
homogeneity of regional intensity. The desired contours of each nucleus can be obtained by minimizing this energy
functional. The proposed algorithm has been tested on human cervical nuclei images. Experiment results show that our
method can separate touching or overlapping ellipse-like nuclei from each other accurately, and the tests on noisy and
textured nuclei images also demonstrate its robustness. The resulting segmentation contours are ellipses in different sizes
and directions, therefore the shapes of the nuclei have been preserved to a certain degree. The algorithm can be naturally
extended to color images, and also has the potential to deal with the separation for overlapping nuclei of other shapes.
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