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Phase contrast microscope is one of the most universally used instruments to observe long-term cell movements in different solutions. Most of classic segmentation methods consider a homogeneous patch as an object, while the recorded cell images have rich details and a lot of small inhomogeneous patches, as well as some artifacts, which can impede the applications. To tackle these challenges, this paper presents a hierarchical mergence approach (HMA) to extract homogeneous patches out and heuristically add them up. Initially, the maximum region of interest (ROI), in which only cell events exist, is drawn by using gradient information as a mask. Then, different levels of blurring based on kernel or grayscale morphological operations are applied to the whole image to produce reference images. Next, each of unconnected regions in the mask is applied with Otsu method independently according to different reference images. Consequently, the segmentation result is generated by the combination of usable patches in all informative layers. The proposed approach is more than simply a fusion of the basic segmentation methods, but a well-organized strategy that integrates these basic methods. Experiments demonstrate that the proposed method outperforms previous methods within our datasets.

Hierarchical Mergence Approach to Cell Detection in Phase Contrast Microscopy Images