Automated cell nuclei segmentation in pleural effusion cytology using active appearance model

Abstract

Pleural effusion is common in clinical practice and it is a frequently encountered specimen type in cytopathological assessment. In addition to being time-consuming and subjective, this assessment also causes inter-observer and intra-observer variability, and therefore an automated system is needed. In visual examination of cytopathological images, cell nuclei present significant diagnostic value for early cancer detection and prevention. So, efficient and accurate segmentation of cell nuclei is one of the prerequisite steps for automated analysis of cytopathological images. Nuclei segmentation also yields the following automated microscopy applications, such as cell counting and classification. In this paper, we present an automated technique based on active appearance model (AAM) for cell nuclei segmentation in pleural effusion cytology images. The AAM utilizes from both the shape and texture features of the nuclei. Experimental results indicate that the proposed method separates the nuclei from background effectively. In addition, comparisons are made with the segmentation methods of thresholding-based, clustering-based and graph-based, which show that the results obtained with the AAM method are actually more closer to the ground truth.