Research & Publication

Abstract:Pap smear test has been widely 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 important fields of medical image processing. This paper reviews the segmentation and classification methods available in the literature related to cervical cell image analysis. Some segmentation techniques are applied on single cervical cell images. Other techniques are designed to use in single cell or overlapped or multiple cell images. Many classification schemes are proposed for automatic categorization of the cells into two classes: normal versus abnormal. The main aim of all these techniques is to build an automated Pap smear analysis system which analyses Pap smear slides in a short time without fatigue, providing consistent and objective classification results.