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This article is part of the supplement: Proceedings of the 11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy

Open Access Proceedings

Pap smear cell image classification using global MPEG-7 descriptors

Luz H Camargo1, Gloria Diaz2 and Eduardo Romero3*

Author Affiliations

1 Faculty of Engineering – Cra. 7 No.40 53, Universidad Distrital Francisco José de Caldas, Bogotá D. C. – Colombia

2 Faculty of Engineering – Cra. 5 No.21 38, Universidad Central, Bogotá D. C. – Colombia

3 Bioingenium, National University of Colombia, Cra 30 No 45 03-Ciudad Universitaria, Faculty of Medicine - Building 471, National University of Colombia, Bogotá DC - Colombia

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Diagnostic Pathology 2013, 8(Suppl 1):S38  doi:10.1186/1746-1596-8-S1-S38

Published: 30 September 2013

First paragraph (this article has no abstract)

Several strategies have been previously applied for classifying cervical cytology cells, all pursuing a nucleus segmentation. Sanchez sets regions [1] using a simple threshold [2], a procedure broadly adapted to different techniques: a local adaptive segmentation nuclei procedure [3], seed growing [4], mathematical morphology [5], a Hough transform [6], and active contours [7]. Jantzen and Dounias propose several cell features as morphometric descriptors, including the nucleus and cytoplasm areas, nucleus / cytoplasm proportion, nucleus and cytoplasm brightnesses, smaller and larger nucleus/cytoplasm diameters, nucleus and cytoplasm roundness, nucleus and cytoplasm perimeters, nucleus position, nucleus/cytoplasm maxima and minima. Nevertheless, these morphometric characteristics require a previous accurate segmentation, hardly achieved by human intervention using commercial software such as CHAMP (Cytology and Histology Modular Analysis Package, Aarhus, Denmark) or DIMAC (Digital Image Company) [8,9].