A New Approach for Edge Detection of Color Microscopic Image Using Modified Pulse Coupled Neural Networks
In this paper, a novel algorithm for microscopic color cell image edge detection based on Pulse Coupled Neural Networks (PCNN) is presented. Microscopic color image edge detection has proven to be a difficult task due to uneven brightness, cross-color existing between cell boundary and background caused by dyeing and noise. To solve these problems, this paper originally employs an improved PCNN model called multi-dimensional PCNN to implement edge detection of microscopic color cell image. PCNN is an advanced approach which aims at processing color images parellelly rather than separately dispose signal to every channel because it is characterized by synchronous neuronal burst and multi-dimensional convolution. To test the feasibility and effectiveness of multi-dimensional PCNN on edge detection of microscopic color cell images, experiments of several microscopic cell images are carried out. Empirical results show that multi-dimensional PCNN outperforms classical methods in terms of anti-noise and the accuracy of weak edge detection.
PCNN multi-dimensional convolution color edge detection vector Gradient
Feiyan Cheng Zhaobin Wang Yide Ma Lizhen Yang Qingxiang Gao
School of Information Science and Engineering Lanzhou University Lanzhou China School of Life Science and Technology Lanzhou University Lanzhou China
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）