Detection and separation of overlapping cells based on contour concavity for Leishmania images.
Life scientists often must count cells in microscopy images, which is a tedious and time-consuming task. Automatic approaches present a solution to this problem. Several procedures have been devised for this task, but the majority suffer from performance degradation in the case of cell overlap. In this article, we propose a method to determine the positions of macrophages and parasites in fluorescence images of Leishmania-infected macrophages. The proposed strategy is primarily based on blob detection, clustering, and separation using concave regions of the cells' contours. In comparison with the approaches of Nogueira (Master's thesis, Department of University of Porto Computer Science, 2011) and Leal et al. (Proceedings of the 9th international conference on Image Analysis and Recognition, Vol. II, ICIAR'12. Berlin, Heidelberg: Springer-Verlag; 2012. pp. 432-439), which also addressed this type of image, we conclude that the proposed methodology achieves better performance in the automatic annotation of Leishmania infections.
Department of Computer Science, IT-Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal.
João C
JC
Neves
Helena
H
Castro
Ana
A
Tomás
Miguel
M
Coimbra
Hugo
H
Proença
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007891:Leishmania / Q000302:isolation & purification* / Q000472:pathogenicity / Q000648:ultrastructure; D007896:Leishmaniasis / Q000175:diagnosis* / Q000469:parasitology / Q000473:pathology; D008264:Macrophages / Q000473:pathology / Q000648:ultrastructure*; D008856:Microscopy, Fluorescence / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods
Leishmania; annotation; image analysis; pattern recognition