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乳腺X线图像肿块建模与分割

Keywords: 乳腺X线图像,乳腺癌早期检测,肿块分割,数学建模,均值漂移,无边缘活动轮廓模型

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Abstract:

提出了一种基于模型分析与均值漂移聚类的乳腺肿块分割方法.该方法根据肿块的临床特征表现建立了肿块的数学模型,并通过多重滤波实现肿块的准确定位.在此基础上,结合均值漂移算法获得的像素点集合,筛选出初始肿块.最后利用无边缘活动轮廓模型准确分割出肿块.实验采用通用的MIAS数据库进行算法性能测试,结果表明本文方法在保证较低假阳性率的同时,肿块检测真阳性率高于形态学成分分析方法.此外,本文方法分割出的肿块边界完整,可满足临床检验与诊断需求.

References

[1]  American Cancer Society. Breast cancer facts and figures 2009-2010[R]. Atlanta: American Cancer Society, 2010.
[2]  Cancer Research U.K. Breast cancer mortality statistics 2010[EB/OL].[2012-02-01].http://info.cancerres-earchuk.org/cancerstats/types/breast/mortality/index. htm.
[3]  Globocan 2008. Breast cancer incidence and mortality worldwide[EB/OL].[2012-02-01].http://globocan.iarc.fr/factsheets/cancers/breast.asp.
[4]  Biswas S K, Mukherson D P. Recognizing architectural distortion in mammogram: a multiscale texture modeling approach with GMM[J]. IEEE Transactions on Biomedical Engineering, 2011,58(7):2023-2030.
[5]  National Cancer Institute. National cancer Institute fact sheet: improving methods for breast cancer detection and diagnosis[EB/OL].[2012-02-01]. http://www.cancer.gov/cancertopics/screening/breast.
[6]  Guliato D, Rangayyan R M, Carvalho J D,et al. Polygonal modeling of contours of breast tumors with the preservation of spicules[J]. IEEE Transactions on Biomedical Engineering, 2008,55(1):14-20.
[7]  Eltonsy N H, Tourassi G D, Elmaghraby A S. A concentric mophology model for the decection of masses in mammography[J]. IEEE Transactions on Medical Imaging, 2007,26(6):880-889.
[8]  Gao X B, Wang Y, Li X L, et al. On combining morphological component analysis and concentric morphology model for mammographic mass detection[J]. IEEE Transactions on Information Technology in Biomedicine, 2010,14(2):266-273.
[9]  王大凯,侯榆青,彭进业.图像处理的偏微分方程方法[M].北京:科学出版社,2008:103-106. Wang Dakai, Hou Yuqing, Peng Jinye. Image processing using partial differential equations[M]. Beijing: Science Press, 2008:103-106.(in Chinese)
[10]  Xu S Z, Pei C D. Hierarchical matching for automatic detection of masses in mammograms[C]//Proceedings of 2011 International Conference on Electrical and Control Engineering (ICECE). Yichang: IEEE Press, 2011:4523-4526.
[11]  Sample J T. Computer assisted screening of digital mammogram images[R]. Baton Rouge, USA: Louisiana State University,2003.
[12]  Sahba F, Venetsanopoulos A. Mean shift based algorithm for mammographic breast mass detection[C]//Proceedings of 2010 17th IEEE International Conference on Image Processing (ICIP).Hongkong: IEEE, 2010:3629-3632.
[13]  李乡儒,吴福朝,胡占义.均值漂移算法的收敛性[J].软件学报,2005(3):365-374. Li Xiangru, Wu Fuchao, Hu Zhanyi. Convergence of a mean shift algorithm[J]. Journal of Software, 2005(3):365-374. (in Chinese)
[14]  Terada T, Fukumizu Y, Yamauchi H, et al. Detecting mass and its region in mammograms using mean shift segmentation and iris filter[C]//Proceedings of 2010 International Symposium on Communications and Information Technologies (ISCIT). Tokyo: IEEE Press,2010:1176-1179.
[15]  Suckling J, Boggis C R M, Hutt I, et al. Mammographic image analysis society (MIAS) database[EB/OL].[2010-03-20].http://peipa.essex.ac.uk/ipa/pix/mias.

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