Segmentation of cervical nuclei using SLIC and pairwise regional contrast

Ratna Saha, Mariusz Bajger, Gobert Lee

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)

Abstract

A framework to detect and segment nuclei from cervical cytology images is proposed in this study. Poor contrast, spurious edges, degree of overlap, and intensity inhomogeneity make the nuclei segmentation task more complex in overlapping cell images. The proposed technique segments cervical nuclei by merging over-segmented SLIC superpixel regions using a novel region
merging criteria based on pairwise regional contrast and image gradient contour evaluations. The framework was evaluated using the first overlapping cervical cytology image segmentation challenge - ISBI 2014 dataset. The result shows that the proposed framework outperforms the state-of-the-art algorithms in nucleus detection and segmentation accuracies.

Original languageEnglish
Pages3422-3425
Number of pages4
DOIs
Publication statusPublished - 2018
Event2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA, Honolulu, United States
Duration: 17 Jul 201821 Jul 2018

Conference

Conference2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Abbreviated titleEMBC
CountryUnited States
CityHonolulu
Period17/07/1821/07/18

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