摘要
细胞图像由于其内在特性,一直是图像分割的难点。为了更好地分割与标识细胞图像,采用双层结构,第1层基于传统的分裂合并算法的思想,引入脉冲耦合神经网络并简化对图像分裂,引入MS模型并简化对分裂区域进行合并,得到一个粗分割图像;第2层利用Canny算子提取图像的边界,将长度大于阈值的边界视作细胞边界,反之视作伪边界,得到一个不连续的边界。最后在两层结果的基础上,利用数学形态学的方法,进行骨架提取与细化,最终得到区域分割与标识的结果。实验用5幅细胞图像作为测试,对算法的参数选择做了详细讨论,并就正确标识率和运行时间与基于区域的分割、基于先验模型的分割做对比,表明本文算法更优。
Cellular images are the challenges in the field of image segmentation because of their intrinsic properties.A dual-layer structure was adopted in order to solve this problem：the 1^st layer based on traditional Split-Merge method introduces and simplifies pulse coupled neural network to split the images,and Mumford-Shah model to merge the split areas.The output is a coarse segmented image.The 2^nd layer extracts the edges via Canny operator and regards the edges whose length are larger than given threshold as cell edge while the opposite as false-edges caused by noises.The output is a non-continuous edge.The two layers are combined together to obtain the final labeling results via skeleton extraction and thinning from mathematical morphology.Experiments with 5 different cell images demonstrate the proposed algorithm outperforms than region-based segment and a priori model-based segment on the basis of the discussion on parameters selection and comparison with those methods in terms of correct labeling rate and computation time.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2010年第8期1885-1889,共5页
Journal of System Simulation
基金
国家自然科学基金(60872075)
国家863计划(2008AA01Z227)高等学校科技创新工程重大项目培育资金项目(706028)
关键词
细胞图像分割
图像标识
分裂合并法
脉冲耦合神经网络
cellular images segmentation
image identification
split-merge method
pulse coupled neural network
