Multiplicative gradient based edge detection method for medical ultrasound image" /> 基于乘性梯度的医学超声图像边缘检测算法
Please wait a minute...
浙江大学学报(工学版)
生物医学工程、化学工程     
基于乘性梯度的医学超声图像边缘检测算法
弓晓虹1, 郑音飞1, 秦佳乐2, 周浩1
1. 浙江大学 生物医学工程教育部重点实验室,浙江 杭州 310027; 2. 浙江大学附属妇产科医院 超声影像科,浙江 杭州310006
Multiplicative gradient based edge detection method for medical ultrasound image
GONG Xiao-hong1, ZHENG Yin-fei1, QIN Jia-le2, ZHOU Hao1
1.Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China; 2. Department of Ultrasound, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
 全文: PDF(4555 KB)   HTML
摘要:

通过改进传统乘性梯度(MG)算子,并结合Canny算子的处理结果,准确、快速地提取了医学超声图像的边缘.通过提升乘性梯度算子模板的维度,提高对弱边缘的检测能力;综合考虑乘性梯度算子和Canny算子的运算结果,提高边缘检测的准确度.为了验证该算法的有效性,对仿真及在体超声图像进行边缘提取实验,将结果与其他抗噪性较好的边缘检测方法进行对比.实验结果表明:对于含有不同强度斑点噪声的超声图像,该算法的边缘检测准确度可达75%以上,具有较好的实时性,适用于对医学超声图像进行快速、准确的边缘检测.

Abstract:

An accurate and fast edge detection method for medical ultrasound images was proposed based on a modified multiplicative gradient (MG) operator and the traditional Canny. The dimension of the template of the MG operator was increased to improve the performance in weak edge detecting. The results of Canny and MG operators were combined to improve the accuracy of the edge detection. The proposed method was evaluated based on simulated and in vivo ultrasound images. Experimental results showed that the accuracy of the proposed method was more than 75% for different speckle noise level. The results of the edge detection can be obtained in real-time by using the proposed method.

出版日期: 2014-10-01
:  TP 242  
基金资助:

中央高校基本科研业务费专项资金资助项目(2011FZA5007, 2013FZA5017, 2014FZA5019);国家“十二五”科技支撑计划资助项目(2011BAI12B02).

通讯作者: 郑音飞,男,副教授     E-mail: zyfnjupt@126.com
作者简介: 弓晓虹(1988—),女,硕士生,从事医学超声图像处理的研究.E-mail:gongxiao_hong_ok@126.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

弓晓虹, 郑音飞, 秦佳乐, 周浩. 基于乘性梯度的医学超声图像边缘检测算法[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2014.10.023.

GONG Xiao-hong, ZHENG Yin-fei, QIN Jia-le, ZHOU Hao.

Multiplicative gradient based edge detection method for medical ultrasound image
. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2014.10.023.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2014.10.023        http://www.zjujournals.com/eng/CN/Y2014/V48/I10/1871

[1] CRONAN J J. Ultrasound: is there a future in diagnostic imaging? [J]. Journal of the American College of Radiology, 2006, 3(9): 645-646.
[2] MOREAU J F. Re: “ultrasound: is there a future in diagnostic imaging?” [J]. Journal of the American College of Radiology, 2007, 4(1): 78-79.
[3]  YU Yong-jian, SCOTT T. Edge detection in ultrasound imagery using the instantaneous coefficient of variation [J]. IEEE Transaction on Image Processing, 2004, 13(12): 1640-1655.
[4] CHEN Zhi-gang, CUI Yue-li, CHEN Ai-hua. An multi-scale edge detection approach [C] ∥ 2012 International Conference on Solid State Devices and Materials Science. Macao: [s.n.], 2012: 1616-1620.
[5] NES P G. Fast multi-scale edge-detection in medical ultrasound signals [J]. Signal Processing, 2012, 92(10): 2394-2408.
[6] LOPEZ-MOLINA C, BAETS B, BUSTINCE H, et al. Multiscale edge detection based on Gaussian smoothing and edge tracking [J]. Knowledge-Based Systems, 2013, 44(12): 101-111.
[7] VERMA O P, HANMANDLU M, SULTANIA A K, et al. A novel fuzzy system for edge detection in noisy image using bacterial foraging [J]. Multidimensional Systems and Signal Processing, 2011, 24(1): 181-198.
[8] TALAI Z, TALAI A. A fast edge detection using fuzzy rules [C] ∥ Proceedings of the Communications, Computing and Control Applications. Hammamet: IEEE, 2011: 15.
[9] RAY K. Unsupervised edge detection and noise detection from a single image [J]. Pattern Recognition, 2013, 46(8): 2067-2077.
[10] 何文浩, 原魁, 邹伟. 自适应阈值的边缘检测算法及其硬件实现 [J]. 系统工程与电子技术, 2009, 31(1): 233-237.
HE Wen-hao, YUAN Kui, ZOU Wei. Self-adaptive threshold edge detection and its implementation in hardware [J]. Systems Engineering and Electronics, 2009, 31(1): 233-237.
[11] WU P, CHEN Q. A novel SVM-based edge detection method [J]. Physics Procedia, 2012, 24(C): 2075-2082.
[12] LI W, WANG C, WANG Q, et al. An edge detection method based on optimized BP neural network [C]∥ International Symposium on Information Science and Engineering. Shanghai: IEEE, 2008: 40-44.
[13] MORA M, CRDOVA-LEPE F, DEL-VALLE R. A non-Newtonian gradient for contour detection in images with multiplicative noise [J]. Pattern Recognition Letters, 2012, 33(10): 1245-1256.
[14] CRDOVA-LEPE F. From quotient operation toward a proportional calculus [J]. Internetional Journal of Mathematics, Game Theory and Algebra, 2009, 18(6): 527-536.
[15] CRDOVA-LEPE F. The multiplicative derivative as a measure of elasticity in economics [J]. Theaeteto Atheniensi Mathematica (TEMAT), 2004, 2(3) online.
[16] CANNY J. A computational approach to edge detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI-8(6): 679-698.
[17] JOSHI S R, KOJU R. Study and comparison of edge detection algorithms [C] ∥ 2012 3rd Asian Himalayas International Conference on Internet. Kathmandu: IEEE, 2012: 15.
[18] LOPEZ-MOLINA C, DE BAETS B, BUSTINCE H. Quantitative error measures for edge detection [J]. Pattern Recognition, 2013, 46(4): 1125-1139.
[1] 高德东, 李强, 雷勇, 徐飞, 白辉全. 基于几何逼近法的斜尖柔性穿刺针运动学研究[J]. 浙江大学学报(工学版), 2017, 51(4): 706-713.
[2] 汤志东, 贠超. 全自动快换装置快速接头技术综述[J]. 浙江大学学报(工学版), 2017, 51(3): 461-470.
[3] 张湧涛, 宋志伟, 王一, 粘山坡. 基于空间网格的机器人工作点位姿标定方法[J]. 浙江大学学报(工学版), 2016, 50(10): 1980-1986.
[4] 徐显金, 吴龙辉, 杨小俊, 汤亮, 杨永峰. 高压直流巡检机器人的磁力驱动方法[J]. 浙江大学学报(工学版), 2016, 50(10): 1937-1945.
[5] 朱雨时,杨灿军,吴世军,徐晓乐,周璞哲,单鑫. 水柱测量中的水下滑翔机转向性能[J]. 浙江大学学报(工学版), 2016, 50(9): 1637-1645.
[6] 贾松敏,卢迎彬,王丽佳,李秀智,徐涛. 分层特征移动机器人行人跟踪[J]. 浙江大学学报(工学版), 2016, 50(9): 1677-1683.
[7] 丁夏清,杜卓洋,陆逸卿,刘山. 基于混合势场的移动机器人视觉轨迹规划[J]. 浙江大学学报(工学版), 2016, 50(7): 1298-1306.
[8] 刘亚男,倪鹤鹏,张承瑞,王云飞,孙好春. 基于PC的运动视觉一体化开放控制平台设计[J]. 浙江大学学报(工学版), 2016, 50(7): 1381-1386.
[9] 张阿龙, 章明, 乔明杰, 朱伟东, 梅标. 基于视觉测量的环形轨底座位姿标定方法[J]. 浙江大学学报(工学版), 2016, 50(6): 1080-1087.
[10] 江文婷, 龚小谨, 刘济林. 基于增量计算的大规模场景致密语义地图构建[J]. 浙江大学学报(工学版), 2016, 50(2): 385-391.
[11] 黄奇伟, 章明, 曲巍崴, 卢贤刚, 柯映林. 机器人制孔姿态优化与光顺[J]. 浙江大学学报(工学版), 2015, 49(12): 2261-2268.
[12] 李巍, 赵志刚, 石广田, 孟佳东. 多机器人并联绳牵引系统的运动学及动力学解[J]. 浙江大学学报(工学版), 2015, 49(10): 1916-1923.
[13] 马子昂,项志宇. 光流测距全向相机的标定与三维重构[J]. 浙江大学学报(工学版), 2015, 49(9): 1651-1657.
[14] 何雪军, 王进, 陆国栋, 陈立. 基于蚁群算法的机器人图像绘制序列优化[J]. 浙江大学学报(工学版), 2015, 49(6): 1139-1145.
[15] 袁康正,朱伟东,陈磊,薛雷,戚文刚. 机器人末端位移传感器的安装位置标定方法[J]. 浙江大学学报(工学版), 2015, 49(5): 829-834.