Nucleus Segmentation and Recognition of Uterine Cervical Pap-Smears

Abstract

The classification of the background and cell areas is very important but difficult problem due to the ambiguity of boundaries. In this paper, the cell region is extracted from an image of uterine cervical cytodiagnosis using the region growing method. Segmented images from background and cell areas are binarized using a threshold value. And the 8-directional tracking algorithm for contour lines is applied to extract the cell area. Each extracted nucleus is transformed to the original RGB space. Then the K-Means clustering algorithm is employed to classify RGB pixels to the R, G, and B channels, respectively. Finally, the Hue information of nucleus is extracted from the HSI models that are transformed using the clustering values in R, G, and B channels. The fuzzy RBF Network is then applied to classify and identify the normal or abnormal nucleus. The result shows that the accuracy of our method is 80% overall and 66% in 5-class problem according to the Bethesda system.