Browsing by Subject "Cervical cancers"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Pap smear test görüntülerinde hücre çekirdeklerinin bölütlenmesi
    (IEEE, 2009-04) Kale, Aslı; Aksoy, Selim; Önder, S.
    Cervical cancer is a preventable disease and the dysplasia it causes can be scanned by using a pap smear test. It can be beneficial to develop a computer-assisted diagnosis system to make the pap smear test robust and widespread. The most fundamental part of such a system is the segmentation of nuclei and cytoplasm in cervical cell images. The aim of this study is to segment the nuclei in such images. First, markers on the nuclei are found by using mathematical morphology operations. Based on the obtained markers, marker-based watershed segmentation and balloon snake model are applied to find the nuclei contours in a data set consisting of cervical cell images. The data set is composed of six classes ranging according to the dysplasia degree of the cells. The results are evaluated according to the relative distance error measure, and the strengths and weakness of the methods are discussed. ©2009 IEEE.
  • Thumbnail Image
    ItemOpen Access
    Segmentation of cervical cell images
    (IEEE, 2010) Kale, A.; Aksoy, S.
    The key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation of cells. In this paper, we propose a two-phase approach to cell segmentation in Pap smear test images with the challenges of inconsistent staining, poor contrast, and overlapping cells. The first phase consists of segmenting an image by a non-parametric hierarchical segmentation algorithm that uses spectral and shape information as well as the gradient information. The second phase aims to obtain nucleus regions and cytoplasm areas by classifying the segments resulting from the first phase based on their spectral and shape features. Experiments using two data sets show that our method performs well for images containing both a single cell and many overlapping cells. © 2010 IEEE.