Nominated texture based cervical cancer classification.

Edwin Jayasingh Mariarputham,Allwin Stephen

doi:10.1155/2015/586928

Edwin Jayasingh Mariarputham, Allwin Stephen

Open Access

https://doi.org/10.1155/2015/586928

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Abstract

Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features) are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern histogram, tamura features, and edge orientation histogram. Few types of support vector machine (SVM) and neural network (NN) classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes.

Highlights

Cervical cancer is one of the most common cancers affecting women worldwide and the most common in developing countries [1]
It is reported that annually 132,000 new cases were diagnosed and 74,000 deaths in India, which is nearly one-third of the global cancer deaths [2]
Localization of cell objects were done in low resolution and boundary detection of cytoplasm and nucleus were done in high resolution

Summary

Introduction

Cervical cancer is one of the most common cancers affecting women worldwide and the most common in developing countries [1]. Many automatic and semiautomatic methods have been proposed in various times to detect various stages of cervical cancer. Plissiti et al [7] have developed the fully automated method to detect the nucleus in Pap smear images. By considering nucleus as the most informative region of the cell, Sobrevilla et al [8] have been presented an algorithm for automatic nuclei detection of cytology cell This algorithm combines color, cytopathologists knowledge, and fuzzy systems which show high performance and more computational speed. The geometric active contours were used as the segmentation tool In this method, localization of cell objects were done in low resolution and boundary detection of cytoplasm and nucleus were done in high resolution. The automatic classification of Pap smear images focuses on marking of single cells into any one of binary classes (normal and abnormal) or multiple classes (based on severity). NN uses the training set which contains inputs, outputs, and the learning rules

Abnormal

Experimental Results and Discussion

Conclusion