[1]杨琴,姜自波,刘承超.基于频域快速解卷的血管提取算法[J].计算机技术与发展,2016,26(03):113-116.
 YANG Qin,JIANG Zi-bo,LIU Cheng-chao. Vessel Extraction Algorithm Based on Fast Frequency-domain Deconvolution[J].,2016,26(03):113-116.
点击复制

基于频域快速解卷的血管提取算法()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年03期
页码:
113-116
栏目:
智能、算法、系统工程
出版日期:
2016-03-10

文章信息/Info

Title:
 Vessel Extraction Algorithm Based on Fast Frequency-domain Deconvolution
文章编号:
1673-629X(2016)03-0113-04
作者:
 杨琴姜自波刘承超
 曲阜师范大学 信息科学与工程学院
Author(s):
 YANG QinJIANG Zi-boLIU Cheng-chao
关键词:
 频率域解卷纹理增强中值滤波OTSU图像分割
Keywords:
 frequency domain deconvolutiontexture enhancement median filteringOTSU image segmentation
分类号:
TP391
文献标志码:
A
摘要:
 为了方便识别新生血管,从而有效地对疾病进行诊断,所以需要将血管图像从背景图像中分割出来。文中提出一种在提高血管分辨率的基础上进行血管分割的算法。首先通过特定公式在梯度图像上对血管图像进行纹理增强,这是为了在之后的图像分割中,能识别细微的血管,使其不被忽略掉;然后对模糊图像进行频域上的快速解卷去模糊,消除成像仪器在成像过程中对图像清晰度的影响;由于图像在传输过程中会产生噪声,因此为了去除噪声对血管分割的影响,接着对血管图像进行了中值滤波操作;最后使用最大类间方差法来进行血管的分割操作,因为最大类间方差法可以有效地将图像前景和背景分割开。通过实验对比,直观上证明了该算法在血管分割中的有效性。
Abstract:
 In order to identify new blood vessels,thus effectively diagnosing disease,it is necessary to segment the vascular image from the background image. A segmentation algorithm based on improved resolution of the vessel is proposed. First the texture is enhanced for blood vessel image through specific formula in the gradient image,in order to identify small blood vessels and not ignore it in the image segmentation later. Secondly,the image deconvolution in frequency domain is made to obtain a sharp image,eliminating the influence on image clarity of imaging instrument in the imaging process. Then so as to remove the influence of noise on the vessel segmentation,a fil-tering operation is carried on vascular image. Finally,the maximum class variance method is used to segment vessels,because it can effec-tively distinguish the image foreground and background. The effectiveness of the algorithm in vessel segmentation is verified intuitively by experimental comparisons.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
 ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(03):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
 LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(03):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
 HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(03):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
 HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(03):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
 LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(03):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
 WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(03):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
 LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(03):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
 SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(03):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
 YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(03):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
 YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(03):47.

更新日期/Last Update: 2016-06-12