# An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation

## TL;DR

This paper introduces an unsupervised method for segmenting overlapping cervical cell cytoplasm, effectively handling poor contrast and overlaps to improve boundary detection in cervical smear images.

## Contribution

It proposes a novel unsupervised segmentation approach combining a modified Otsu method and level set evolution for accurate cytoplasm boundary detection.

## Key findings

- Effective segmentation of overlapping cells achieved
- High accuracy on ISBI 2015 dataset
- Automated approach reduces manual intervention

## Abstract

The poor contrast and the overlapping of cervical cell cytoplasm are the major issues in the accurate segmentation of cervical cell cytoplasm. This paper presents an automated unsupervised cytoplasm segmentation approach which can effectively find the cytoplasm boundaries in overlapping cells. The proposed approach first segments the cell clumps from the cervical smear image and detects the nuclei in each cell clump. A modified Otsu method with prior class probability is proposed for accurate segmentation of nuclei from the cell clumps. Using distance regularized level set evolution, the contour around each nucleus is evolved until it reaches the cytoplasm boundaries. Promising results were obtained by experimenting on ISBI 2015 challenge dataset.