Histology images are used to identify biological structures present in living organisms - cells, tissues, organs, and parts of organs. E-Learning systems can use images to aid teaching how morphological features relate to function and understanding which features are most diagnostic of organs. The structure of cells varies according to the type and function of the cell. Automatic cell segmentation is one of the challenging tasks in histology image processing. This problem has been addressed using morphological gradient, region-based methods and shape-based method approaches, among others. In this paper, automatic segmentation of nuclei of epithelial cells is addressed by including morphological information. Image segmentation is commonly evaluated in isolation. This is either done by observing results, via manual segmentation or via some other goodness measure that does not rely on ground truth images. Expert criteria along with images manually segmented are used to validate automatic segmentation results. Experimental results show that the proposed approach segments epithelial cells in a close way to expert manual segmentations. An average sensitivity of 76% and an average specificity of 77% were obtained on a selected set of images.
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