23 March 2016 Automatic choroid cells segmentation and counting in fluorescence microscopic image
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In this paper, we proposed a method to automatically segment and count the rhesus choroid-retinal vascular endothelial cells (RF/6A) in fluorescence microscopic images which is based on shape classification, bottleneck detection and accelerated Dijkstra algorithm. The proposed method includes four main steps. First, a thresholding filter and morphological operations are applied to reduce the noise. Second, a shape classifier is used to decide whether a connected component is needed to be segmented. In this step, the AdaBoost classifier is applied with a set of shape features. Third, the bottleneck positions are found based on the contours of the connected components. Finally, the cells segmentation and counting are completed based on the accelerated Dijkstra algorithm with the gradient information between the bottleneck positions. The results show the feasibility and efficiency of the proposed method.
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Jianjun Fei, Jianjun Fei, Weifang Zhu, Weifang Zhu, Fei Shi, Fei Shi, Dehui Xiang, Dehui Xiang, Xiao Lin, Xiao Lin, Lei Yang, Lei Yang, Xinjian Chen, Xinjian Chen, } "Automatic choroid cells segmentation and counting in fluorescence microscopic image", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97911B (23 March 2016); doi: 10.1117/12.2216172; https://doi.org/10.1117/12.2216172

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