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Red blood cell segmentation using Active Appearance Model

The red blood cell segmentation is an important technology for automatic cell counting, classification and analysis in clinical examination. In this paper, we propose a red blood cell segmentation method based on Active Appearance Models (AAM). The AAM can effectively describe the shape information and texture information of the red blood cell, and separate cells from background precisely. Experimental results show that the AAM can extract cells from background effectively. Besides, compared with many traditional image segmentation methods, the AAM method provides a closed form of the boundary of the cell, so it is no need to perform boundary tracking for cell counting, measurement and analysis.