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.
2015
37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)
Milano
25-29 August, 2015
8135
8138
Tonti, Simone; DI CATALDO, Santa; Macii, Enrico; Ficarra, Elisa
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1240378
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