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.
|Number of pages||4|
|Publication status||Published - 26 Oct 2018|
|Event||2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - |
Duration: 18 Jul 2018 → …
|Conference||2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)|
|Period||18/07/18 → …|