Tonti Simone, Di Cataldo Santa, Macii Enrico, Ficarra Elisa
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:8135-8. doi: 10.1109/EMBC.2015.7320282.
Automated HEp-2 mitotic cell recognition in IIF images is an important and yet scarcely explored step in the computer-aided diagnosis of autoimmune disorders. Such step is necessary to assess the goodness of the HEp-2 samples and helps the early diagnosis of the most difficult or ambiguous cases. In this work, we propose a completely unsupervised approach for HEp-2 mitotic cell recognition that overcomes the problem of mitotic/non-mitotic class imbalance due to the limited number of mitotic cells. Our technique automatically selects a limited set of candidate cells from the HEp-2 slide and then applies a clustering algorithm to identify the mitotic ones based on their texture. Finally, a second stage of clustering discriminates between positive and negative mitoses. Experiments on public IIF images demonstrate the performance of our technique compared to previous approaches.
在间接免疫荧光（IIF）图像中自动识别HEp-2有丝分裂细胞是自身免疫性疾病计算机辅助诊断中一个重要但尚未充分探索的步骤。这一步骤对于评估HEp-2样本的质量很有必要，有助于早期诊断最困难或最模糊的病例。在这项工作中，我们提出了一种完全无监督的HEp-2有丝分裂细胞识别方法，该方法克服了由于有丝分裂细胞数量有限导致的有丝分裂/非有丝分裂类别不平衡问题。我们的技术会自动从HEp-2载玻片中选择一组有限的候选细胞，然后应用聚类算法根据其纹理识别有丝分裂细胞。最后，第二阶段的聚类区分阳性和阴性有丝分裂。在公共IIF图像上进行的实验证明了我们的技术与以前方法相比的性能。
