摘要
结合新一代神经网络———脉冲耦合神经网络和色彩空间分解技术,提出了一种对植物染色切片图像切实有效的自动分割新方案.该方案首次引入最大熵决策机制,同时充分利用了彩色图像的色彩信息,极大地提高图像分割的准确性,简化了传统算法的复杂度,缩短了运行时间,易于在实时系统上实现.实验结果表明该方案与传统的方案相比,分割结果比较准确.同时既保证了整体轮廓,又保留了重要细节.
An approach of biological microsection auto -segmentation is proposed by combining the neural network： PCNN and color space decomposition technology. Decision-making mechanism based on the maximal entropy is suggested to make full use of the color information of image. The mechanism simplifies the complexity of traditional methods and improves the speed greatly. Object image is efficiently selected from component images via this mechanism. Then the object image is segmented by PCNN. The experimental results show that the image is segmented more exactly compared with that segmented by traditional method, and important image details are conserved. With the high speed, the approach can be implemented conveniently in the real-time system.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2009年第9期173-175,共3页
Journal of Harbin Institute of Technology
基金
国家自然科学基金资助项目(60572011)
关键词
生物切片
PCNN
图像分割
边缘检测
色彩空间
biological microsection
PCNN
image segmentation
edge detection
color space
