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一种基于上下文信息的乳腺肿块ROI检测方法 被引量：3

A context based method for ROI detection in digitized mammograms
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摘要 传统的ROI(region of interest)检测方法忽略了图像中的上下文信息,为了解决这个问题,本文利用概率潜在语义分析(probability latent semantic analysis,PLSA)来对图像中的每块区域周围的图像特征进行分析,并利用其作为上下文特征来辅助ROI的检测。实验表明,该方法与直接进行分类相比,能够取得更好的分类效果。 In order to solve the problem that context information was often missed in region of interest（ROI） detection,probability latent semantic analysis（PLSA） was employed to extract the context information from surrounding regions,and the context information was used as context features to aid ROI detection.Experimental results showed that the context information can effectively improve the accuracy of ROI detection.
作者 郭乔进 丁轶 李宁
机构地区 南京大学计算机科学与技术系计算机软件新技术国家重点实验室
出处 《山东大学学报（理学版）》 CAS CSCD 北大核心 2010年第7期70-75,共6页 Journal of Shandong University(Natural Science)
基金 国家自然科学基金资助项目(60875011)
关键词 乳腺数字图像 ROI检测 概率潜在语义分析 mammogram ROI-detection probability latent semantic analysis
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献11

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同被引文献38

  • 1Hofmann T. Probabilistic latent semantic indexing [C] //Proe of Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York:ACM, 1999: 50-57.
  • 2Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation [J]. Journal of Machine Learning Research, 2003, 3(4/5): 993-1022.
  • 3Sivic J, Russell B C, Efros A A, et al. Discovering objects and their location in images [C] //Proc of IEEE Int Conf on Computer Vision, vol 1. Piscataway, NJ: IEEE, 2005: 370- 377.
  • 4Li Cao, Li Feifei. Spatially coherent latent topic model for concurrent segmentation and classification of objects and scenes [C] //Proc of Int Conf on Computer Vision. Piscataway, NJ: IEEE, 2007:1-8.
  • 5Boiman O, Shechtman E, Irani M. In defense of nearest neighbor based image classification [C] //Proc of IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2008:1-8.
  • 6Verbeek J, Triggs W. Region classification with Markov field aspect models[C] //Proe of IEEE Conf on Computer Vision and Pattern Recognition. Piseataway, NJz IEEE, 2007, 1-8.
  • 7Mackey L. Latent Dirichlet Markov random fields for semi supervised image segmentation and object recognition [R]. Berkeley: Computer Science at University of California, Berkeley, 2007.
  • 8Blei D M, Jordan M I. Modeling annotated data [C]//Proc of the ACM SIGIR Conf on Research and Development in Informaion Retrieval. New York: ACM, 2003z 127-134.
  • 9Lafferty J, McCallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data [C] //Proc of Int Conf on Machine Learning. Piscataway, NJ: IEEE, 20011 282-289.
  • 10Kumar S, Hebert M. Discriminative fields for modeling spatial dependencies in natural images [C] //Advances in Neural Information Processing Systems. Cambridge, MA: MIT press, 2004:1531-1538.

引证文献3

二级引证文献6

山东大学学报（理学版）
2010年 第7期

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