Nucleus Segmentation of Cervical Cytology Images Based on Multi-scale Fuzzy Clustering Algorithm

Overview

Journal Bioengineered

Date 2020 Apr 14

PMID 32279589

Citations 5

Authors

Jinjie Huang

Tao Wang

Dequan Zheng

Yongjun He

Affiliations

Soon will be listed here.

Abstract

In the screening of cervical cancer cells, accurate identification and segmentation of nucleus in cell images is a key part in the early diagnosis of cervical cancer. Overlapping, uneven staining, poor contrast, and other reasons present challenges to cervical nucleus segmentation. We propose a segmentation method for cervical nuclei based on a multi-scale fuzzy clustering algorithm, which segments cervical cell clump images at different scales. We adopt a novel interesting degree based on area prior to measure the interesting degree of the node. The application of these two methods not only solves the problem of selecting the categories number of the clustering algorithm but also greatly improves the nucleus recognition performance. The method is evaluated by the IBSI2014 and IBSI2015 public datasets. Experiments show that the proposed algorithm has greater advantages than the state-of-the-art cervical nucleus segmentation algorithms and accomplishes high accuracy nucleus segmentation results.

Citing Articles

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Pixel-wise segmentation of cells in digitized Pap smear images.

Harangi B, Bogacsovics G, Toth J, Kovacs I, Dani E, Hajdu A Sci Data. 2024; 11(1):733.

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Cervical cell nuclei segmentation based on GC-UNet.

Zhang E, Xie R, Bian Y, Wang J, Tao P, Zhang H Heliyon. 2023; 9(7):e17647.

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Privacy Preserved Cervical Cancer Detection Using Convolutional Neural Networks Applied to Pap Smear Images.

Alsubai S, Alqahtani A, Sha M, Almadhor A, Abbas S, Mughal H Comput Math Methods Med. 2023; 2023:9676206.

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Pap Smear Images Classification Using Machine Learning: A Literature Matrix.

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