Automated classification of Pap smear images to detect cervical dysplasia. (January 2017)

• Record Type: Journal Article
• Authors: Bora, Kangkana; Chowdhury, Manish; Mahanta, Lipi B.; Kundu, Malay Kumar; Das, Anup Kumar
• Is Part Of: Computer methods and programs in biomedicine. Volume 138(2017)
• Page Start: 31
• Page End: 47
• Publication Date: 2017-01
• Subjects: Pap smear -- MSER -- Ripplet transform -- Ensemble classification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.10.001
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An automated Pap smear classifier is proposed to detect cervical dysplasia. Study performed on both cell as well as smear level indigenous real images collected from two diagnostic centers. Analysis is being performed on shape, texture and color features which includes 121 total numbers of features. An ensemble classifier is designed using LSSVM, MLP and Random Forest using weighted majority voting. Classification reflects the established Bethesda pathological classification of cervical cancer. Abstract: 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.