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In the pulse-coupled neural network model, the linking coefficient β plays an important role. According to the relative entropy D (P , Q : t ) and standard deviation, educe an estimate formula that to set the linking coefficient β automatically in the simplified pulse-coupled neural network model. Experimental results show that the algorithm gets a better visual effect when dealing liver hydatid disease image,showing great adaptability., correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=xT3YRL8NtILIlqFY791XuQ==, pdfFileSize=1725247, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. College of Medical Engineer Technology, Xinjiang Medical University, Urumqi 830011, China;2. School of Information Science & Engineering, Central South University, Changsha 410083, China;3. Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Urumqi 830054, China, fund=null, authors=TIAN Xianghua1 , HAMIT Murat1 , ZHU Congxu2 , KONG Dewei3 , authorsList=TIAN Xianghua;HAMIT Murat;ZHU Congxu;KONG Dewei), CN=ArticleExt(id=1242128305894261300, articleId=1242128303725806110, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=脉冲耦合神经网络对肝包虫图像分割, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=在应用脉冲耦合神经网络模型分割图像的研究中,确定模型的参数是一个难点问题,其中连接系数β 在脉冲耦合神经网络中起着重要的作用。本文使用最小交叉熵D (P , Q ; t )和标准差,简化了脉冲耦合神经网络模型的连接系数β 的估计公式,该方法可以自动确定并简化脉冲耦合神经网络模型的连接系数β 。实验结果表明,该方法对肝包虫医学图像的分割效果显著,能获得较好的视觉结果并具有较强的普适性。, correspAuthors=null, authorNote=null, correspAuthorsNote=木拉提·哈米提,教授,研究方向为医学图像处理及信号分析,电子信箱:murat.h@163.com;朱从旭,教授,研究方向为混沌系统理论技术及其应用、图像处理,电子信箱:zhucongxu@126.com, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, 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科技导报
| 研究论文 2011, 29(36): 33-37
脉冲耦合神经网络对肝包虫图像分割
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田翔华1 , 木拉提·哈米提1 , 朱从旭2 , 孔德伟3
作者信息
1. 新疆医科大学医学工程技术学院,乌鲁木齐 830011;2. 中南大学信息科学与工程学院,长沙 410083;3. 新疆医科大学第一附属医院放射科,乌鲁木齐 830054
通讯作者:
木拉提·哈米提,教授,研究方向为医学图像处理及信号分析,电子信箱:murat.h@163.com;朱从旭,教授,研究方向为混沌系统理论技术及其应用、图像处理,电子信箱:zhucongxu@126.com
Applications of Pulse-Coupled Neural Network in Image Segmentation for Liver Hydatid
Affiliations
出版时间: 2011-12-28
doi: 10.3981/j.issn.1000-7857.2011.36.005
文章导航
在应用脉冲耦合神经网络模型分割图像的研究中,确定模型的参数是一个难点问题,其中连接系数β 在脉冲耦合神经网络中起着重要的作用。本文使用最小交叉熵D (P , Q ; t )和标准差,简化了脉冲耦合神经网络模型的连接系数β 的估计公式,该方法可以自动确定并简化脉冲耦合神经网络模型的连接系数β 。实验结果表明,该方法对肝包虫医学图像的分割效果显著,能获得较好的视觉结果并具有较强的普适性。
脉冲耦合神经网络
/
图像分割
/
医学图像
/
肝包虫病
In the application of pulse-coupled neural network(PCNN),it is difficult to set the parameter of pulse-coupled neural network. In the pulse-coupled neural network model, the linking coefficient β plays an important role. According to the relative entropy D (P , Q : t ) and standard deviation, educe an estimate formula that to set the linking coefficient β automatically in the simplified pulse-coupled neural network model. Experimental results show that the algorithm gets a better visual effect when dealing liver hydatid disease image,showing great adaptability.
PCNN
/
image segmentation
/
medical image
/
liver hydatid disease
田翔华;木拉提·哈米提;朱从旭;孔德伟.
脉冲耦合神经网络对肝包虫图像分割.
科技导报,
2011
, 29
(36)
: 33
-37
.
DOI: 10.3981/j.issn.1000-7857.2011.36.005
TIAN Xianghua;HAMIT Murat;ZHU Congxu;KONG Dewei.
Applications of Pulse-Coupled Neural Network in Image Segmentation for Liver Hydatid[J].
Science & Technology Review ,
2011
, 29
(36)
: 33
-37
.
DOI: 10.3981/j.issn.1000-7857.2011.36.005
2011年第29卷第36期
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文章信息
doi: 10.3981/j.issn.1000-7857.2011.36.005
接收时间:2011-08-23
首发时间:2011-12-28
出版时间:2011-12-28
收稿日期:2011-08-23
修回日期:2011-11-25
通讯作者:
木拉提·哈米提,教授,研究方向为医学图像处理及信号分析,电子信箱:murat.h@163.com;朱从旭,教授,研究方向为混沌系统理论技术及其应用、图像处理,电子信箱:zhucongxu@126.com
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2011.36.005
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2种不同金属材料的力学参数
科 Family 属数 Number of genus 种数 Number of species 占总种数比例 Percentage of total species (%) 属 Genus 种数 Number of species 占总种数比例 Percentage of total species (%) 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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