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Automated classification of Pap smear images to detect cervical dysplasia
KTH, Skolan för teknik och hälsa (STH).ORCID-id: 0000-0002-7767-3399
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2017 (Engelska)Ingår i: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 138, s. 31-47Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Background and objectives: The present study proposes an intelligent system for automatic categorization of Pap smear images to detect cervical dysplasia, which has been an open problem ongoing for last five decades. Methods: The classification technique is based on shape, texture and color features. It classifies the cervical dysplasia into two-level (normal and abnormal) and three-level (Negative for Intraepithelial Lesion or Malignancy, Low-grade Squamous Intraepithelial Lesion and High-grade Squamous Intraepithelial Lesion) classes reflecting the established Bethesda system of classification used for diagnosis of cancerous or precancerous lesion of cervix. The system is evaluated on two generated databases obtained from two diagnostic centers, one containing 1610 single cervical cells and the other 1320 complete smear level images. The main objective of this database generation is to categorize the images according to the Bethesda system of classification both of which require lots of training and expertise. The system is also trained and tested on the benchmark Herlev University database which is publicly available. In this contribution a new segmentation technique has also been proposed for extracting shape features. Ripplet Type I transform, Histogram first order statistics and Gray Level Co-occurrence Matrix have been used for color and texture features respectively. To improve classification results, ensemble method is used, which integrates the decision of three classifiers. Assessments are performed using 5 fold cross validation. Results: Extended experiments reveal that the proposed system can successfully classify Pap smear images performing significantly better when compared with other existing methods. Conclusion: This type of automated cancer classifier will be of particular help in early detection of cancer.

Ort, förlag, år, upplaga, sidor
Elsevier, 2017. Vol. 138, s. 31-47
Nyckelord [en]
Pap smear, MSER, Ripplet transform, Ensemble classification
Nationell ämneskategori
Biomedicinsk laboratorievetenskap/teknologi
Identifikatorer
URN: urn:nbn:se:kth:diva-199468DOI: 10.1016/j.cmpb.2016.10.001ISI: 000389285300005PubMedID: 27886713Scopus ID: 2-s2.0-84994045082OAI: oai:DiVA.org:kth-199468DiVA, id: diva2:1067876
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QC 20170123

Tillgänglig från: 2017-01-23 Skapad: 2017-01-09 Senast uppdaterad: 2024-03-18Bibliografiskt granskad

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Chowdhury, Manish

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Computer Methods and Programs in Biomedicine
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