NDLI: Texture-based feature extraction of smear images for the detection of cervical cancer

Content Provider	IET Digital Library
Author	Arya, Mithlesh Mittal, Namita Singh, Girdhari
Abstract	In India, cervical cancer is the second most common type of cancer in females. Pap smear is a simple cytology test for the detection of cancer in its early stages. To obtain the best results from the Pap smear, expert pathologist are required. Availability of pathologist in India is far below the required numbers, especially in rural parts. In this paper, multiple texture-based features are introduced for the extraction of relevant and informative features from single-cell images. First-order histogram, GLCM, LBP, Laws, and DWT are used for texture feature extraction. These methods help to recognise the contour of the nucleus and cytoplasm. ANN and SVM are used to classify the single-cell images either normal or cancerous based on the trained features. ANN and SVM are used on every single feature as well as on the combination of all features. Best results are obtained with a combination of all features. The system is evaluated on generated dataset MNITJ, containing 330 single cervical cell images and also on publicly available benchmark Herlev data set. Experimental results show that the proposed texture-based features give significantly better results in cervical cancer detection when compared with state of the art shape-based features regarding accuracy.
Starting Page	1049
Ending Page	1059
Page Count	11
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 8, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5349
Journal	IET Computer Vision
Publisher Date	2018-09-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ANN Artificial Neural Network Automated Cervical Cancer Detection System Biology And Medical Computing Biomedical Measurement And Imaging Cancer Cancerous Cell Cellular Biophysics Computer Vision And Image Processing Technique Cytoplasm Discrete Wavelet Transform Edge Detection Feature Extraction First-order Histogram Function Theory And Analysis Grey-level Cooccurrence Matrix Health Physics Image Classification Image Processing Image Recognition Image Segmentation Image Texture India Informative Feature Integral Transforms Knowledge Engineering Technique Law Textural Energy Measures Learning in AI Local Binary Pattern Machine Learning Technique Machine Learning-based Diseases Diagnosis System Medical And Biomedical Uses of Field Medical Image Medical Image Processing Multiple Texture-based Feature Method Neural Computing Technique Neural Nets Normal Cell Nucleus Pap Smear Patient Diagnosis Patient Diagnostic Method And Instrumentation Radiations Radioactivity Relevant Feature Shape-based Feature Single Cervical Cell Image Single Feature Single-cell Image Smear Image Support Vector Machine SVM Texture Description Texture Feature Extraction Texture-based Feature Extraction Trained Feature
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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