Inflammatory Cell Extraction and Nuclei Detection in Pap Smear Images

Dwiza Riana, Marina E. Plissiti, Christophoros Nikou, Dwi H. Widyantoro, Tati Latifah R. Mengko and Oemie Kalsoem
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Dwiza Riana: Bandung Institute of Technology, Bandung, Indonesia
Marina E. Plissiti: Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece
Christophoros Nikou: Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece
Dwi H. Widyantoro: Bandung Institute of Technology, Bandung, Indonesia
Tati Latifah R. Mengko: Bandung Institute of Technology, Bandung, Indonesia
Oemie Kalsoem: Veteran Bandung Laboratory of Pathology, Bandung, Indonesia

International Journal of E-Health and Medical Communications (IJEHMC), 2015, vol. 6, issue 2, 27-43

Abstract: The automated diagnosis of cervical cancer in Pap smear images is a difficult though extremely important procedure. In order to obtain reliable diagnostic information, the nuclei and their characteristics must be correctly identified and evaluated. However, the presence of inflammatory and overlapping cells in these images complicates the detection process. In this work, a segmentation algorithm is developed to extract the inflammatory cells and enable accurate nuclei detection. The proposed algorithm is based on the combination of gray level thresholding and the definition of a distance rule, which entails in the identification of inflammatory cells. The results indicate that our method significantly simplifies the nuclei detection process, as it reduces the number of inflammatory cells that may interfere.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jehmc0:v:6:y:2015:i:2:p:27-43

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