A robust convergence index filter for breast cancer cell segmentation
COnvergence INdex (COIN) filter, a successful tool for cell localization, evaluates the degree of convergence of the gradient vectors within the neighborhood (region of support) toward a pixel of interest. All previous efforts were to increase the adaptability of the region of support to make the COIN filter robust and accurate. However, improving the quality of the image gradient map was ignored, which results in poor performance of the members of the COIN family in noisy settings. We propose a new Robust Convergence Index (RCI) filter that tailors the COIN filter in a noisy environment by (a) spreading the gradient vectors within non-homogeneous object regions by convolving an Aggregated Edge Probability Map (AEPM) with an edge preserving gradient vector kernel, and (b) increasing the convergence of the gradient vectors through the integration of the sine and cosine distribution as well as the magnitude of the gradient vectors. AEPM is computed through the consensus of the responses of a number of edge detectors over a wide range of scales, which lessens the effects of clutter by enforcing higher weights to the actual edges, and a non-parametric Kernel Density Estimation (KDE) is used to compute the edge probability map. Experimental results demonstrate that it obtains state-of-the-art performance.