Comparative analysis of cell segmentation using absorption and color images in fine needle aspiration cytology

Segmentation of cytological smears plays a critical role in the automated analysis of histological abnormalities by fine needle aspiration cytology. However, smears obtained from fine needle aspiration biopsy are often contaminated with blood. Segmentation of such an image is not a trivial task and the false positive rate could be high if the blood cells cannot be correctly separated from the rest of the sample. Moreover, the fine textured nature of the cell chromatin gives it a non-uniform intensity appearance in both color and gray images. In this paper, we propose an enhanced watershed approach to remove background noise by using short wavelength spectral image and the computed absorption image to improve segmentation accuracy. We also demonstrate a color image segmentation method by applying watershed to the minima imposed aggregation image. Results of segmentation on 20 images of cytological smears are presented and the accuracy compared for the two methods.