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      Feature Quantification and Abnormal Detection on Cervical Squamous Epithelial Cells

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          Abstract

          Feature analysis and classification detection of abnormal cells from images for pathological analysis are an important issue for the realization of computer assisted disease diagnosis. This paper studies a method for cervical squamous epithelial cells. Based on cervical cytological classification standard and expert diagnostic experience, expressive descriptors are extracted according to morphology, color, and texture features of cervical scales epithelial cells. Further, quantificational descriptors related to cytopathology are derived as well, including morphological difference degree, cell hyperkeratosis, and deeply stained degree. The relationship between quantified value and pathological feature can be established by these descriptors. Finally, an effective method is proposed for detecting abnormal cells based on feature quantification. Integrated with clinical experience, the method can realize fast abnormal cell detection and preliminary cell classification.

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          The challenges of organising cervical screening programmes in the 15 old member states of the European Union.

          Marc Arbyn, Matejka Rebolj, Inge de Kok … (2009)
          Cervical cancer incidence and mortality can be reduced substantially by organised cytological screening at 3 to 5 year intervals, as was demonstrated in the Nordic countries, the United Kingdom, the Netherlands and parts of Italy. Opportunistic screening, often proposed at yearly schedules, has also reduced the burden of cervical cancer in some, but not all, of the other old member states (belonging to the European Union since 1995) but at a cost that is several times greater. Well organised screening programmes have the potential to achieve greater participation of the target population at regular intervals, equity of access and high quality. Despite the consistent evidence that organised screening is more efficient than non-organised screening, and in spite of the Cancer Screening Recommendations of the European Council, health authorities of eight old member states (Austria, Belgium, France, Germany, Greece, Luxembourg, Portugal and Spain) have not yet started national organised implementation of screening for cervical cancer. A decision was made by the Irish government to extend their pilot programme nationally while new regional programmes commenced in Portugal and Spain. Introduction of new methods of prevention, such as HPV screening and prophylactic HPV vaccination, can reduce the burden further, but this will require a high level of organisation with particular attention needed for the maximisation of population coverage, compliance with evidence-based guidelines, monitoring of data enabling continued evaluation and improvement of the preventive programmes.
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            Automatic cervical cell segmentation and classification in Pap smears.

            Nipon Theera-Umpon, Thanatip Chankong, Sansanee Auephanwiriyakul (2014)
            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.
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              Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering.

              George Nikou, Antonia Charchanti, Marina E Plissiti (2011)
              In this paper, we present a fully automated method for cell nuclei detection in Pap smear images. The locations of the candidate nuclei centroids in the image are detected with morphological analysis and they are refined in a second step, which incorporates a priori knowledge about the circumference of each nucleus. The elimination of the undesirable artifacts is achieved in two steps: the application of a distance-dependent rule on the resulted centroids; and the application of classification algorithms. In our method, we have examined the performance of an unsupervised (fuzzy C-means) and a supervised (support vector machines) classification technique. In both classification techniques, the effect of the refinement step improves the performance of the clustering algorithm. The proposed method was evaluated using 38 cytological images of conventional Pap smears containing 5617 recognized squamous epithelial cells. The results are very promising, even in the case of images with high degree of cell overlapping.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Math Methods Med
                Journal ID (iso-abbrev): Comput Math Methods Med
                Journal ID (publisher-id): CMMM
                Title: Computational and Mathematical Methods in Medicine
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1748-670X
                ISSN (Electronic): 1748-6718
                Publication date (Print): 2015
                Publication date (Electronic): 22 March 2015
                Volume: 2015
                Electronic Location Identifier: 941680
                Affiliations
                1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
                2Department of Informatics, University of Hamburg, 22527 Hamburg, Germany
                Author notes

                Academic Editor: Shengyong Chen

                Author information
                http://orcid.org/0000-0001-5384-0757
                http://orcid.org/0000-0003-2567-5357
                Article
                DOI: 10.1155/2015/941680
                PMC ID: 4385601
                SO-VID: 529877ad-7e84-4fab-8712-783cb02ecb45
                Copyright © Copyright © 2015 Mingzhu Zhao et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 26 June 2014
                Date accepted : 12 August 2014
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Applied mathematics
                Data availability:
                ScienceOpen disciplines: Applied mathematics

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