Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology especially for cervical cancer screening from pap-smears. Manual assessment of pap-smears is labour intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. A critical prerequisite in automated analysis of pap-smears is nucleus and cytoplasm segmentation, which is the basis of cervical cancer screening. This paper articulates a potent approach to the segmentation of cervical cells into nucleus and cytoplasm using a quadtree decomposition approach with statistical measures.
Choosing an appropriate quadtree decomposition strategy was a great challenge and a novel task in the proposed approach. The image is pre-processed using an enhanced median filter and is decomposed based on the mean, maximum entropy and the variance statistical measures of the pixels in the subtree. As a result, highly efficient and segmentations of acceptable performance were obtained. Comparison of the segmented nucleus and cytoplasm with the ground truth nucleus and cytoplasm segmentations resulted into a Zijdenbos similarity index of greater than 0.9034 and 0.9498 for nucleus and cytoplasm segmentation respectively.
Given the accuracy of the classifier in segmenting the nucleus which plays an important role in cervical cancer diagnosis and classification, the classifier can be adapted for automated systems for cervical cancer diagnosis and classification. The method serves as a basis for first level segmentation of cervical cells for diagnosis and classification of cervical cancer from pap-smears.