Tonti Simone, Di Cataldo Santa, Bottino Andrea, Ficarra Elisa
Dept. of Computer and Control Engineering, Politecnico di Torino, Cso Duca degli Abruzzi 24, 10129 Torino, Italy.
Dept. of Computer and Control Engineering, Politecnico di Torino, Cso Duca degli Abruzzi 24, 10129 Torino, Italy.
Comput Med Imaging Graph. 2015 Mar;40:62-9. doi: 10.1016/j.compmedimag.2014.12.005. Epub 2015 Jan 6.
The automatization of the analysis of Indirect Immunofluorescence (IIF) images is of paramount importance for the diagnosis of autoimmune diseases. This paper proposes a solution to one of the most challenging steps of this process, the segmentation of HEp-2 cells, through an adaptive marker-controlled watershed approach. Our algorithm automatically conforms the marker selection pipeline to the peculiar characteristics of the input image, hence it is able to cope with different fluorescent intensities and staining patterns without any a priori knowledge. Furthermore, it shows a reduced sensitivity to over-segmentation errors and uneven illumination, that are typical issues of IIF imaging.
间接免疫荧光（IIF）图像分析的自动化对于自身免疫性疾病的诊断至关重要。本文提出了一种解决方案，用于解决该过程中最具挑战性的步骤之一，即通过自适应标记控制的分水岭方法对HEp-2细胞进行分割。我们的算法能自动使标记选择流程符合输入图像的特殊特征，因此无需任何先验知识就能应对不同的荧光强度和染色模式。此外，它对过分割错误和光照不均的敏感度降低，而过分割错误和光照不均是IIF成像中的典型问题。
