Cell Migration Analysis using a Statistical Level-Set Segmentation on a Wavelet-Based Structure Tensor Feature Space
In this paper, a new noise-resilient cell migration analysis scheme for bladder cancer cells is presented. The proposed scheme is based on texture segmentation in the wavelet domain using structure tensor features and adaptive statistical level-set segmentation. The proposed method extracts the region of interest where the cells are clustering, at different time instances, and computes the overall migration cell rate. For this purpose, the structure tensor data is processed using a trous wavelet filtering, which speeds up the algorithm as compared to existing nonlinear diffusion filters with the same accuracy. The proposed scheme is robust to noise and natural artifacts in the bladder cancer cell images. Moreover, the scheme can be applied successfully to images with poor contrast and high cell concentrations, even when the cells are overlapping and tiny. Simulation results are presented to show the performance of the proposed scheme.