Cervical Cell Classification Using Features Related to Morphometry and Texture of Nuclei

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• 作者：Juan Valentín Lorenzo-Ginori (18)
  Wendelin Curbelo-Jardines (18)
  José Daniel López-Cabrera (18)
  Sergio B. Huergo-Suárez (18)
  
• 关键词：Papanicolaou test ; features ; texture ; dimensionality reduction ; classifiers
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8259
• 期：1
• 页码：230-237
• 全文大小：304KB
• 作者单位：Juan Valentín Lorenzo-Ginori (18)
  Wendelin Curbelo-Jardines (18)
  José Daniel López-Cabrera (18)
  Sergio B. Huergo-Suárez (18)
  
  18. Center for Studies on Electronics and Information Technologies, Universidad Central “Marta Abreu” de Las Villas (UCLV), Cuba
  
• ISSN：1611-3349

文摘

The Papanicolaou test is used for early prediction of cervical cancer. Computer vision techniques for automating the microscopy analysis of cervical cells in this test have received great attention. Cell segmentation is needed here in order to obtain appropriate features for classification of abnormal cells. However, accurate segmentation of the cell cytoplasm is difficult, due to cell overlapping and variability of color and intensity. This has determined a growing interest in classifying cells using only features from the nuclei, which are easier to segment. In this work, we classified cells in the pap-smear test using a combination of morphometric and Haralick texture features, obtained from the nucleus gray-level co-occurrence matrix. A comparison was made among various classifiers using these features and data dimensionality reduction through PCA. The results obtained showed that this combination can be a promising alternative in order to automate the analysis of cervical cells.