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MRI中的肿瘤边缘的自动检测 被引量:3

Automatic Edge-Detection of Tumor in MRI
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摘要 MRI用于临床诊治时 ,肿瘤大小的自动检测一直是尚未圆满解决的问题 ,其中的关键技术是肿瘤边缘的自动检测 .边缘检测是计算机初级视觉中的一个难题 ,一直受到图像处理研究人员的关注 .虽然边缘检测的算子很多 ,但都是在噪声滤除与边缘检出之间作权衡取舍 .本研究中提出了一种结合形态滤波器和广义模糊算子自动检测MRI中的肿瘤边缘的新方法 ,它能在保留边缘细节的同时最大程度地抑制噪声 .实验结果表明 ,在噪声抑制和边缘点检出的折衷中 。 Measuring the tumors in MRI automatically has always been a problem and no satisfactory solution has yet been offered. The key technology is the automatic detection of the tumor edges. Edge_detection is also a big problem in computational low_level vision and has always attracted the attention of image_processing researchers. There are many edge_detectors, but they are limited in making a balance between noise_suppressing and edge_detection. This paper presents a new method, which detects the edges of tumor in MRI using morphological filter and generalized fuzzy operator. It can preserve edge details while suppressing the noises. Experimental result shows that the new method does better than traditional methods.
出处 《华南理工大学学报(自然科学版)》 EI CAS CSCD 北大核心 2001年第4期6-8,共3页 Journal of South China University of Technology(Natural Science Edition)
基金 国家自然科学基金资助项目! (19972 0 44 ) 广东省自然科学基金资助项目! (990 6 6 8)
关键词 核磁共振成像 形态学滤波器 广义模糊算子 边缘检测 医学图像分析 肿瘤检测 magnetic resonance imaging morphological filter generalized fuzzy operator edge_detection
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参考文献3

  • 1陈武凡.彩色图像边缘检测的新算法[J].中国科学:A辑,1995,25(2):219-224.
  • 2Zhu Yan,IEEE Trans Medical Image,1997年,16卷,1期,55页
  • 3陈武凡,中国科学.A,1995年,15卷,2期,219页

共引文献10

同被引文献35

  • 1韩培友,张曙光,郝重阳,董桂云.一种双线性快速模糊增强图像边界检测最新算法[J].计算机应用研究,2004,21(6):134-135. 被引量:1
  • 2王长军,朱善安.基于统计模型和GVF-Snake的彩色目标检测与跟踪[J].中国图象图形学报,2006,11(1):13-18. 被引量:6
  • 3陈武凡.彩色图像边缘检测的新算法[J].中国科学:A辑,1995,25(2):219-224.
  • 4Zeng Xiang-Yan, Chen Yen-Wei, Nakao Zensho. Image feature representation by the subspace of nonlinear PCA [ C ] //Proceeding of the 16th International Conference on Pattern Recognition. Quebec City : IEEE, 200i : 228- 231.
  • 5Dony Rober D, Haykin Simon. Optimally adaptive transform coding [ J]. IEEE Transactions on Image Processing, 1995,4(10) : 1 358-1 370.
  • 6Oja E. The nonlinear PCA learning rule and signal separation-mathematical analysis [ J]. Neurocomputing, 1997, 17:25-45.
  • 7Vapnik V N. The nature of statistical learning theory [ M]. New York :Springer-Verlag, 1995.
  • 8Seholkopf Bernhard, Smola Alexander, Muller Klaus- Robert. Nonlinear component analysis as a kernel eigenvalue problem [ J ]. Neural Computing, 1998,10:1 299- 1319.
  • 9Kwok James Tin-Yau,Tsang Ivor Wai-Hung. The pre-image problem in kernel methods [J]. IEEE Transactions on Neural Networks, 2003,, 15 ( 6 ) : 1517-1525.
  • 10Chen Yen-wei, Zeng Xiang-yan, Lu Han-qing. Edge detection and texture segmentation based on independent component analysis [ C]//Proceeding of the 16th International Conference on Pattern Recognition. Quebec City: IEEE,2002:351-354.

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