Unsupervised Segmentation of Overlapping Cervical Cell Cytoplasm

TL;DR

This paper introduces an unsupervised method for segmenting overlapping cervical cell cytoplasm by combining EDF imaging, a modified Otsu method for nuclei detection, and level set models for cytoplasm segmentation, addressing challenges of cell overlap and poor contrast.

Contribution

It proposes a novel unsupervised segmentation approach that integrates EDF imaging, a modified Otsu method, and level set models for improved cervical cell analysis.

Findings

Effective segmentation of nuclei using modified Otsu method

Accurate cytoplasm segmentation based on nucleus detection

Addresses overlapping cells and poor contrast issues

Abstract

Overlapping of cervical cells and poor contrast of cell cytoplasm are the major issues in accurate detection and segmentation of cervical cells. An unsupervised cell segmentation approach is presented here. Cell clump segmentation was carried out using the extended depth of field (EDF) image created from the images of different focal planes. A modified Otsu method with prior class weights is proposed for accurate segmentation of nuclei from the cell clumps. The cell cytoplasm was further segmented from cell clump depending upon the number of nucleus detected in that cell clump. Level set model was used for cytoplasm segmentation.