Comput Methods Programs Biomed - Automatic cervical cell segmentation and classification in Pap smears.

Tópicos

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Resumo

Cervical cancer is one of the leading causes of cancer death in females worldwide. The disease can be cured if the patient is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is Papanicolaou test or Pap test. In this research, a method for automatic cervical cancer cell segmentation and classification is proposed. A single-cell image is segmented into nucleus, cytoplasm, and background, using the fuzzy C-means (FCM) clustering technique. Four cell classes in the ERUDIT and LCH datasets, i.e., normal, low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL), and squamous cell carcinoma (SCC), are considered. The 2-class problem can be achieved by grouping the last 3 classes as one abnormal class. Whereas, the Herlev dataset consists of 7 cell classes, i.e., superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. These 7 classes can also be grouped to form a 2-class problem. These 3 datasets were tested on 5 classifiers including Bayesian classifier, linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural networks (ANN), and support vector machine (SVM). For the ERUDIT dataset, ANN with 5 nucleus-based features yielded the accuracies of 96.20% and 97.83% on the 4-class and 2-class problems, respectively. For the Herlev dataset, ANN with 9 cell-based features yielded the accuracies of 93.78% and 99.27% for the 7-class and 2-class problems, respectively. For the LCH dataset, ANN with 9 cell-based features yielded the accuracies of 95.00% and 97.00% for the 4-class and 2-class problems, respectively. The segmentation and classification performances of the proposed method were compared with that of the hard C-means clustering and watershed technique. The results show that the proposed automatic approach yields very good performance and is better than its counterparts.

Resumo Limpo

cervic cancer one lead caus cancer death femal worldwid diseas can cure patient diagnos precancer lesion stage earlier common physic examin techniqu wide use screen papanicolaou test pap test research method automat cervic cancer cell segment classif propos singlecel imag segment nucleus cytoplasm background use fuzzi cmean fcm cluster techniqu four cell class erudit lch dataset ie normal low grade squamous intraepitheli lesion lsil high grade squamous intraepitheli lesion hsil squamous cell carcinoma scc consid class problem can achiev group last class one abnorm class wherea herlev dataset consist cell class ie superfici squamous intermedi squamous columnar mild dysplasia moder dysplasia sever dysplasia carcinoma situ class can also group form class problem dataset test classifi includ bayesian classifi linear discrimin analysi lda knearest neighbor knn artifici neural network ann support vector machin svm erudit dataset ann nucleusbas featur yield accuraci class class problem respect herlev dataset ann cellbas featur yield accuraci class class problem respect lch dataset ann cellbas featur yield accuraci class class problem respect segment classif perform propos method compar hard cmean cluster watersh techniqu result show propos automat approach yield good perform better counterpart

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