Data cluster analysis-based classification of overlapping nuclei in Pap smear samples

PubMedID: 25487072Background: The extraction of overlapping cell nuclei is a critical issue in automated diagnosis systems. Due to the similarities between overlapping and malignant nuclei, misclassification of the overlapped regions can affect the automated systems' final decision. In this paper, we present a method for detecting overlapping cell nuclei in Pap smear samples. Method: Judgement about the presence of overlapping nuclei is performed in three steps using an unsupervised clustering approach: candidate nuclei regions are located and refined with morphological operations; key features are extracted; and candidate nuclei regions are clustered into two groups, overlapping or non-overlapping, A new combination of features containing two local minima-based and three shape-dependent features are extracted for determination of the presence or absence of overlapping. F1 score, precision, and recall values are used to evaluate the method's classification performance. Results: In order to make evaluation, we compared the segmentation results of the proposed system with empirical contours. Experimental results indicate that applied morphological operations can locate most of the nuclei and produces accurate boundaries. Independent features significance test indicates that our feature combination is significant for overlapping nuclei. Comparisons of the classification results of a fuzzy clustering algorithm and a non-fuzzy clustering algorithm show that the fuzzy approach would be a more convenient mechanism for classification of overlapping. Conclusion: The main contribution of this study is the development of a decision mechanism for identifying overlapping nuclei to further improve the extraction process with respect to the segmentation of interregional borders, nuclei area, and radius. Experimental results showed that our unsupervised approach with proposed feature combination yields acceptable performance for detection of overlapping nuclei. © 2014 Guven and Cengizler

A fluid dynamics-based deformable model for segmentation of cervical cell images

A novel deformable model for unsupervised segmentation of cervical cells within Pap smear images is presented in this paper. The proposed method is inspired by fluid mechanics and based on the simulation of incompressible fluid flood via grid-based solution of Navier–Stokes equations. In this approach, simulation starts inside the cytoplasmic region and the simulated fluid is attracted toward the cell contours. Unlike most of the other fluid-based methods, gradient magnitude data are not used for extracting topological relief of the image. However, gradient magnitude of the image is still considered as the source for extracting particles. Direction of propagation of the flow is determined by an interaction mechanism based on the permeability rate of these particles. Interaction between fluid and particles guides the advancing fronts of the fluid toward object boundaries. Redefinition of complex topologies with particle groups provides potential of improved segmentation capability and flexibility to the model. We demonstrate the segmentation capability of our model with fully automated and unsupervised experimental setting on Pap smear sample images. Results showed that proposed method may be more adaptive than watershed algorithm and have an improved performance on recovering shape and boundary data of cervical cells. © 2014, Springer-Verlag London.Acknowledgments The authors would like to thank Prof. Dr. Seyda Erdogan of Cukurova University for their valuable support. This study was granted by the Cukurova University Research Foundation