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.