Automated cervical cancer detection through RGVF segmentation and SVM classification

Abstract

Pap smear test has been broadly used for detection of cervical cancer. However, the conventional Pap smear test has several shortcomings including: subjective nature (dependent on individual interpretation), low sensitivity (i.e. ability to detect abnormal changes) and the need for frequent retesting. There has a great effort to automate Pap smear test and it is one of the critical fields of medical image processing. So this paper proposes a method for automatic cervical cancer detection using cervical cell segmentation and classification. A single cervical cell image is segmented into cytoplasm, nucleus and background using Radiating Gradient Vector Flow (RGVF) Snake. Herlev dataset consists of 7 cervical cell classes, i.e. superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ is considered. Different cellular and nuclei features are extracted for training the system. Dataset is tested on Support Vector Machine (SVM) and artificial neural networks (ANN) and Euclidean distance based system to classify seven different types of cells and to segregate abnormal from normal cells.