207 research outputs found
An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation
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.Comment: 4 pages, 4 figures, Biomedical Engineering and Sciences (IECBES),
2016 IEEE EMBS Conference on. IEEE, 201
Inflammatory Cell Extraction in Pap smear Images: A Combination of Distance Criterion and Image Transformation Approach
In order to obtain a diagnosis of cervical cancer information, the characteristics of each cell nucleus must be identified and evaluated properly through a Pap smear test. The presence of inflammatory cells in Pap smear images can complicate the process of identification of cell nuclei in the early detection of cervical cancer. Inflammatory cells need to be eliminated to assist pathologists in reading Pap smear slides. In this work, we developed a novel method to extract the inflammatory cells that allow detection of cell nuclei more accuracy. The proposed algorithm consists of two stages: extraction of inflammatory cells using the distance criterion and image transformation. This experiment applied to the 1358 cells comprising 378 nuclei cells and 980 inflammatory cells from 25 Pap smear images. The results showed that our method can significantly reduce the amount of inflammation that can disrupt the cell nuclei in the detection process. The proposed method has promising results with a sensitivity level of 97% and a specificity of 84.38%
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