Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model.
In this paper, we propose a novel approach to cell image segmentation under severe noise conditions by combining kernel-based dynamic clustering and a genetic algorithm. Our method incorporates a priori knowledge about cell shape. That is, an elliptical cell contour model is introduced to describe the boundary of the cell. Our method consists of the following components: (1) obtain the gradient image; (2) use the gradient image to obtain points which possibly belong to cell boundaries; (3) adjust the parameters of the elliptical cell boundary model to match the cell contour using a genetic algorithm. The method is tested on images of noisy human thyroid and small intestine cells.
Yang
F
F
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, People's Republic of China. fgyang@nlpr.ia.ac.cn
Jiang
T
T
D000465:Algorithms; D048429:Cell Size; D016000:Cluster Analysis; D019295:Computational Biology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008954:Models, Biological; D008957:Models, Genetic; D010338:Pathology, Clinical / Q000706:statistics & numerical data