Ensemble Framework for Unsupervised Cervical Cell Segmentation

In medical image segmentation, preparing ground truths (or masks) is not trivial as it requires expert clinicians to manually label regions-of-interest. Cervical cytology image segmentation is no exception. In this paper, we propose an unsupervised segmentation framework for cervical cell and whole slide segmentation uses an ensemble of three clustering algorithms namely, K-means, K-means++ and Mean Shift clustering. The final cluster centers obtained from these algorithms are used to initialize cluster points for Fuzzy C-means clustering algorithm. The proposed method is evaluated on multiple standard datasets: HErlev Pap Smear dataset and SIPaKMeD Pap Smear dataset. We also evaluated on a whole slide image dataset (source: CMATER-JU laboratory) and our results are promising and comparable. Overall, our results on multiple benchmark datasets justify the viability of the proposed framework.