Multiple Feature Extraction from Cervical Cytology Images by Gaussian Mixture Model

Abstract

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.