Prior guided segmentation and nuclei feature based abnormality detection in cervical cells

Ratna Saha, Mariusz Bajger, Gobert Lee

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

Computer-assisted techniques for cytological analysis and abnormality detection, can help to early diagnose anomalies in cervical smear images. Cell nuclei carry substantial evidence of pre-cancerous changes, thus morphological properties of nuclei are important for accurate diagnostic decision. A novel nucleus feature-based cervical cell classification framework is proposed
in this study. Prior guided segmentation algorithms are employed to accurately detect and segment nucleus. Fuzzy entropy based feature selection technique is used to select most discriminatory features, extracted from segmented nucleus. Five classifiers: k-nearest neighbor (KNN), linear discriminant analysis (LDA), Ensemble, and support vector machine with linear kernel (SVM-linear) and radial basis function kernel (SVM-RBF), are used to detect abnormality in cervical cells. The proposed framework is evaluated using Herlev dataset of 917 cervical cell images and compared with state-of-the-art methods. Results indicate that the proposed framework matches the performance of recent techniques, while segmenting nucleus and classifying Pap smear images using only 10 nucleus features. Therefore, the proposed abnormality detection framework can
assist cytologists in computerized cervical cell analysis, and help with early discovery of any anomaly that may lead to cervical cancer.
Original languageEnglish
Pages742-746
Number of pages5
DOIs
Publication statusPublished - 2019
Event2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) - Athens, Greece, Athens, Greece
Duration: 28 Oct 201930 Oct 2019

Conference

Conference2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
Abbreviated titleBIBE
CountryGreece
CityAthens
Period28/10/1930/10/19

Keywords

  • Cervical cell classification
  • Nucleus segmentation
  • Pap smear
  • Abnormality detection
  • Prior guided segmentation
  • feature extraction
  • Image segmentation

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