Unsupervised segmentation and ordering of cervical cells : Serviks hücrelerinin öğreticisiz olarak bölütlenmesi ve sıralanması
Author
Samet, Nermin
Advisor
Aksoy, Selim
Date
2014Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
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.
Keywords
Pap smear testPap smear image analysis
Cervical cell segmentation
Multi-scale segmentation
Cell grading