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基于LBP算子的医学图像检索方法 被引量：3

Medical Image Retrieval Method Based on Local Binary Pattern Operator
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摘要 在医学信息领域,对医学图像物理特征的研究有助于实现图像的自动分析和基于内容的检索。为此,本文提出了一种基于LBP算子的医学图像检索方法。首先提取图像的LBP加权直方图特征,此特征能够有效地降低图像特征维数,然后使用该特征进行基于内容的图像检索。实验表明,该方法具有较高的检准率和检全率,并且检索时间较短。 In medical informatics, the study of medical images＇physical features contributes to implement auto analysis and content-based retrieval. Therefore, a kind of texture feature extracting algorithm based LBP （Local Binary Pattern ） operator is proposed. First the weighted LBP histogram feature is exacted, which can reduce the dimension of the image vector. Then these features are applied in content-based image retrieval. Experimental resuhs show that the precision and recall of retrieval are high, and the retrieval time is short.
作者 徐先传 张琦
机构地区 中国传媒大学信息工程学院
出处 《微计算机信息》 北大核心 2007年第04X期281-282,223,共3页 Control & Automation
关键词 医学图像 纹理特征 LBP算子 基于内容检索 medical image, texture feature, LBP operator,content-based retrieval.
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献11

  • 1王勇,吕扬生.基于纹理特征的超声医学图像检索[J].天津大学学报（自然科学与工程技术版）,2005,38(1):57-60. 被引量：10
  • 2陈菲.肝癌超声图像识别的特征提取[J].微计算机信息,2006,22(10X):272-274. 被引量：5
  • 3Sapina R.Computing textural features based on co-occurrence matrix for infrared images[A].In:Proceeding of the2nd International Symposium on Image and Signal Process2ing and Analysis[C].Pula,Croatia:2001.373-376.
  • 4Manjunath B S,Ma W Y.Textures features for browsing and retrieval of image data[J].IEEE Trans on PAM I,1996,(8):837-842.
  • 5Tamura H,Mori S,Yamawaki T.Texture features corresponding to visual percep tion[J].IEEE Trans on Systems,Man and Cybernetics,1978,8(6):460-473.
  • 6T.Ojala,M.Pietikainen.Unsupervised Texture Segmentation Using Feature Distributions[J].Pattern Recognition,1999(32),477-486.
  • 7Filip Florea,Constantin Vertan,Comparison of Histogram-based Feture Sets for Medical Image Modality Categorization[J].IEEE 2005.
  • 8C.Vertan and N.Boujemaa,Upgrading color distributions for imageretrieval:Can we do better?[J].Advmces in visual Infomarion Sysrems,R.Laurini,Ed..vol.LNCS 1929.Lyon,France:Springer Verlag,2000.178-188
  • 9C.Vatan.A.Stoica.C.Fernandez-Maloigne.Color texhre recognitionand indexing by fuzzy color spatial dislributirms[J].Pme.ofEUSIPCO 2002.vol.3.Toulouse.France,3-6 Sept.2002.471-174.
  • 10Iain,Fundamentals of Digifal Image Processing[B].Englewood CliffsNJ.Prentice Hall Intl..1989.

二级参考文献19

  • 1Farrokhnia F. A multi-channel filtering approach to texture Segmentation[A]. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition [ C ]. Maul, Hawaii:1991. 364-370.
  • 2Dum D, Higgins W E, Wakeley J. Texture segmentation using 2-D Gabor elementary functions [ J i. IEEE Transactions on PAMI,1994,16( 2 ) :130-149.
  • 3Daugman J G. Uncertainty relation for resolution in space,spatial frequency, and orientation optimized by two dimensional visual cortical filters [ J ]. J of the Optical Society ofAmerica A, 1985, 2(7) : 160-169.
  • 4Hamamoto Y. A Gabor filter-based method for recognizing bandwritten numereals [ J ]. Pattern Recognition, 1998,31(4) :395-400.
  • 5Van Raad V. Design of Gabor wavelets for analysis of texture features in cervical images [A]. In: Proc of the 25th Annual Int Conference of the IEEE on Eng in Med and Biology Society[ C ]. Cancun, Mexico: 2003,1 : 806-809.
  • 6Jain A. Unsupervised texture segmentaion using Gabor illters[J]. Pattern Recognition, 1991,24 ( 12 ) : 1167-1186.
  • 7Li Xuelong. Content-based image retrieval system with new low-level features, new similarity metric and novel feedback learning[A]. In: Proceedings 2002 International Conference on Machine Learning and Cybernetics [ C 1. Beijing: 2002,2 :1 126-1 132.
  • 8Sapina R. Computing textural features based on co-occurrence matrix for infrared images [ A ]. In: Proceeding of the 2nd International Symposium on Image and Signal Processing and Analysis [ C ]. Pula, Croatia: 2001. 373-376.
  • 9Manjunath B S, Ma W Y. Textures features for browsing and retrieval of image data [ J ]. IEEE Trans on PAMI, 1996,(8) :837-842.
  • 10Tamura H, Mori S, Yamawaki T. Texture features corresponding to visual perception [ J ]. IEEE Trans on Systems,Man and Cybernetics, 1978,8 ( 6 ) :460--473.

共引文献12

同被引文献24

  • 1王勇,吕扬生.基于纹理特征的超声医学图像检索[J].天津大学学报（自然科学与工程技术版）,2005,38(1):57-60. 被引量：10
  • 2Sapina R.Computing textural features based on co-occurrence matrix for infrared images [A].In: Proceeding of the 2nd International Symposium on Image and Signal Process2ing and Analysis [C]. Pula, Croatia: 2001. 373-376.
  • 3Manjunath B S,Ma W Y.Textures features for browsing and retrieval of image data[J].IEEE Trans on PAMI, 1996,(8) :837-842.
  • 4Tamura H, Mori S, Yamawaki T. Texture features corresponding to visual pereep tion [J].IEEE Trans on Systems, Man and Cybernetics, 1978, 8 (6) : 460-473.
  • 5Fu Xiaofeng, Wei Wei. Centralized Binary Patterns Embedded with Image Euclidean Distance for Facial Expression Recognition [C].Fourth International conference on Natural Computation, IEEE Computer Society 2008:115-119
  • 6T.Ojala, M.Pietikainen. Unsupervised Texture Segmentation Using Feature Distributions [J]. Pattern Recognition, 1999 (32), 477-486.
  • 7Filip Florea, Constantin Vertan, Comparison of Histogrambased Feture Sets for Medical Image Modality Categorization [J]. IEEE 2005.
  • 8Ojala T,Pietikainen M,Harwood D.A comparative study of texture measures with classification based on feature distributions[J].Pattern Recognition,1996,2(1):51-59.
  • 9Ojala T,Pietikainen M,Maenpaa T.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
  • 10Henning Lategahn,Sebastian Gross.Texture Classification by Modeling Joint Distributions of Local Patterns With Gaussian Mixtures[J].IEEE Transaction on Image Processing,2010,19(6):15481557.

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