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BackgroundCervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.MethodScene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm.ResultsThe evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab.ConclusionsThe major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

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Background: 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.Results: 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. Conclusion: 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.