Cell cluster segmentation based on global and local thresholding for in-situ microscopy

This paper describes a new cell cluster segmentation algorithm based on global and local thresholding for in-situ microscopy. The global threshold is estimated by applying a known Maximum Likelihood Thresholding technique. Assuming that the background pixels around a cluster have similar intensity values, the local threshold used to improve the segmented region after global thresholding is estimated as the average of the intensity values of a set of selected surrounding background pixels of that region. First, all pixels on the border of the segmented region are defined as possible candidates of surrounding background pixels. Then, an algorithm based on RANSAC (RANdom SAmple Consensus) is applied to detect outliers within the candidates. Only the inliers are used for estimation of the local threshold value. The algorithm was applied to real intensity images captured by an in-situ microscope. The experimental results show that the segmentation accuracy improved by 82%.