Saha Ratna, Bajger Mariusz, Lee Gobert
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:3422-3425. doi: 10.1109/EMBC.2018.8513021.
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.
本研究提出了一种从宫颈细胞学图像中检测和分割细胞核的框架。对比度差、伪边缘、重叠程度和强度不均匀性使得重叠细胞图像中的细胞核分割任务更加复杂。所提出的技术使用基于成对区域对比度和图像梯度轮廓评估的新型区域合并标准，通过合并过分割的SLIC超像素区域来分割宫颈细胞核。该框架使用第一个重叠宫颈细胞学图像分割挑战赛——ISBI 2014数据集进行了评估。结果表明，所提出的框架在细胞核检测和分割精度方面优于现有算法。
