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Unsupervised Segmentation of Cervical Cell Images Using Gaussian Mixture Model
Srikanth Ragothaman, Sridharakumar Narasimhan,
Published in IEEE Computer Society
2016
Pages: 1374 - 1379
Abstract
Cervical cancer is one of the leading causes of cancer death in women. Screening at early stages using the popular Pap smear test has been demonstrated to reduce fatalities significantly. Cost effective, automated screening methods can significantly improve the adoption of these tests worldwide. Automated screening involves image analysis of cervical cells. Gaussian Mixture Models (GMM) are widely used in image processing for segmentation which is a crucial step in image analysis. In our proposed method, GMM is implemented to segment cell regions to identify cellular features such as nucleus, cytoplasm while addressing shortcomings of existing methods. This method is combined with shape based identification of nucleus to increase the accuracy of nucleus segmentation. This enables the algorithm to accurately trace the cells and nucleus contours from the pap smear images that contain cell clusters. The method also accounts for inconsistent staining, if any. The results that are presented shows that our proposed method performs well even in challenging conditions. © 2016 IEEE.
About the journal
JournalData powered by TypesetIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
PublisherData powered by TypesetIEEE Computer Society
ISSN21607508
Open AccessNo
Concepts (21)
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    Automation
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    Cells
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    Communication channels (information theory)
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    Computer vision
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    Cost effectiveness
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    Cytology
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    Diseases
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    Gaussian distribution
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    Image analysis
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    Image processing
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    Object recognition
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    Pattern recognition
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    AUTOMATED SCREENING
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    CERVICAL CANCERS
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    CERVICAL CELLS
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    Cost effective
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    GAUSSIAN MIXTURE MODEL
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    NUCLEUS SEGMENTATION
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    PAP SMEAR IMAGES
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    Unsupervised segmentation
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    Image segmentation