title: Unsupervised HEp-2 mitosis recognition in indirect immunofluorescence imaging.
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.
authors.forename: Simone
authors.initials: S
authors.lastname: Tonti
authors.forename: Santa
authors.initials: S
authors.lastname: Di Cataldo
authors.forename: Enrico
authors.initials: E
authors.lastname: Macii
authors.forename: Elisa
authors.initials: E
authors.lastname: Ficarra
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D019084:Fluorescent Antibody Technique, Indirect; D006801:Humans; D008938:Mitosis