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.
Unsupervised HEp-2 mitosis recognition in Indirect Immunofluorescence Imaging / Tonti, Simone; DI CATALDO, Santa; Macii, Enrico; Ficarra, Elisa. - (2015), pp. 8135-8138. (Intervento presentato al convegno 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015) tenutosi a Milano nel 25-29 August, 2015) [10.1109/EMBC.2015.7320282].
Unsupervised HEp-2 mitosis recognition in Indirect Immunofluorescence Imaging
FICARRA, ELISA
2015
Abstract
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.File | Dimensione | Formato | |
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