Evaluation of Expectation Maximization for the Segmentation of Cervical Cell Nuclei

Abstract

As cervical cancer is one of the most common cancers worldwide, screening programs have been established. For that task stained slides of cervical cells are visually assessed under a microscope for dysplastic or malignant cells. To support this challenge, image processing methods offer advantages for objective classification. As the cell nuclei carry a high extent visual information, all depicted cell nuclei need to be delineated. Within this work, the expectation maximization (EM) algorithm is evaluated as a yet unused method for this task. The EM was trained on 33 micrographs, where nuclei were manually annotated as reference. The EM was evaluated with varying parameter for the number of classes and with four different color spaces (RGB, Lab, HSV, polar HSV). Segmentation results for all images and parameters were compared to the ground truth, yielding average accuracy and standard deviation for all cells. The best color spaces were RGB and Lab. The best number of classes to be used in the color space was found to be K = 3. It can be concluded that the EM is an appropriate and useful approach for cell nuclei segmentation, but needs some image post-processing for the elimination of false positives.