Multiple Feature Extraction from Cervical Cytology Images by Gaussian Mixture Model
Multiple Feature Extraction from Cervical Cytology Images by Gaussian Mixture Model
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DOI：
10.1109/wccct.2014.89
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发表时间：
2014-02
期刊：
影响因子：
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通讯作者：
G. Lakshmi;K. Krishnaveni
中科院分区：
文献类型：
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作者：
G. Lakshmi;K. Krishnaveni
本文介绍了从宫颈细胞学图像中自动提取细胞质和细胞核多特征的方法。图像的边缘通过边缘锐化滤波器增强。然后利用期望最大化和K均值聚类的高斯混合模型将图像分割成背景、细胞核和细胞质。已经确定了多个和单个宫颈细胞学细胞的特征。对于多个细胞图像，计算细胞核与细胞质的比率。从单个细胞核中提取细胞核和细胞质的中心、周长、面积、平均强度等特征的混合。这些特征可用于确定癌症的阶段。
In this paper, methods for automated extraction of multiple features of cytoplasm and nuclei from cervical cytology images are described. Edges of the image are enhanced by Edge Sharpening filter. Then Gaussian mixture model using Expectation Maximization and K-means clustering is used to segment the image into its components as background, nucleus and cytoplasm. Features have been identified for both multiple and single cervical cytology cells. For multiple cell images, nucleus to cytoplasm ratio is calculated. A mixture of features like center, perimeter, area, mean intensity of nucleus and cytoplasm are extracted from cells with single nucleus. These features may be used to determine the stage of cancer.