Browsing by Subject "Cell grading"

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Open Access
Unsupervised segmentation and classification of cervical cell images
(Elsevier BV, 2012-12) Gençtav, A.; Aksoy, S.; Önder, S.
The Pap smear test is a manual screening procedure that is used to detect precancerous changes in cervical cells based on color and shape properties of their nuclei and cytoplasms. Automating this procedure is still an open problem due to the complexities of cell structures. In this paper, we propose an unsupervised approach for the segmentation and classification of cervical cells. The segmentation process involves automatic thresholding to separate the cell regions from the background, a multi-scale hierarchical segmentation algorithm to partition these regions based on homogeneity and circularity, a binary classifier to finalize the separation of nuclei from cytoplasm within the cell regions. Classification is posed as a grouping problem by ranking the cells based on their feature characteristics modeling abnormality degrees. The proposed procedure constructs a tree using hierarchical clustering, then arranges the cells in a linear order by using an optimal leaf ordering algorithm that maximizes the similarity of adjacent leaves without any requirement for training examples or parameter adjustment. Performance evaluation using two data sets show the effectiveness of the proposed approach in images having inconsistent staining, poor contrast, overlapping cells. © 2012 Elsevier Ltd.
Open Access
Unsupervised segmentation and ordering of cervical cells : Serviks hücrelerinin öğreticisiz olarak bölütlenmesi ve sıralanması
(2014) Samet, Nermin
Cervical cancer is the second most common cause of cancer death among women worldwide, and it can be prevented if it is detected and treated in the precancerous stages. Pap smear test is a common, efficient and easy manual screening examination technique which is used to detect dysplastic changes in cervical cells. However, manual analyses of thousands of cells in Pap smear test slides by cyto-technicians is difficult, time consuming and subjective. To overcome these problems, we aim to automate the screening process and provide an ordered nuclei list to help the cyto-experts. Automating the screening procedure has been a longstanding challenge because of complex cell structures where current methods in the literature mostly consider the problem as the segmentation of single isolated cells and leave real challenges of Pap smear images such as poor contrast, inconsistent staining, and unknown number of cells unaddressed. We propose an unsupervised method to accurately segment the nuclei and order them according to their abnormality degree in Pap smear images. The method first uses a multi-scale hierarchical segmentation algorithm for accurate identification of the nuclei. The Pap smear images captured at high level magni- fication have more detailed texture but worse contrast. Contrast is an important property for segmentation and detailed texture is an important property for feature extraction. Therefore, as a solution to the segmentation problem, we proceed in two steps. First, we segment the Pap smear images at low (20x) magnification and eliminate non-nucleus regions based on several features. Then, we switch to high (40x) magnification and obtain a more detailed segmentation of the remaining nuclei. Following segmentation, we extract features for each resulting nucleus. Unlike related works that require a learning phase for classification, our method performs an unsupervised ordering of the nuclei based on features extracted at 40x magnification. We compare different ordering algorithms for ranking the nucleus regions according to their abnormality degrees. We evaluate our segmentation and ordering methods using two data sets. Our results show that the proposed method provides promising results for both segmentation and ordering steps.