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采用自适应核学习相关向量机方法, 结合形态学滤波和Kallergi分簇标准, 研究了乳腺X线图像中微钙化点簇的处理. 首先将微钙化点检测看作一个监督学习问题, 然后应用自适应核学习相关向量机作为分类器判断图像中每一个位置是否为微钙化点并采用形态学处理滤除干扰噪声, 最后对获得的微钙化点采用Kallergi标准进行分簇. 为提高运算速度, 在微钙化点检测时将整个图像分解为多个子图像并行运算, 实现了一种基于自适应核学习相关向量机的微钙化点簇快速处理方法. 实验结果和分析表明, 自适应核学习相关向量机方法算法性能优于相关向量机方法, 特别是实现的快速方法能进一步降低微钙化点簇的处理时间.Using the method of adaptive kernel learning based relevance vector machine (ARVM) and combining the morphological filtering and the clustering criterion recommended by Kallergi, a new algorithm for microcalcification (MC) clusters processing in mammograms is investigated. Firstly, the detection of MC is formulated as a supervised-learning problem. Then the ARVM is used as a classifier to determine whether an MC object is present at each location in the mammogram and a morphological processing is used to remove the isolated spurious pixels. Finally, the identified MC clusters are obtained by Kallergi criterion. To improve the computational speed, a fast processing method based on ARVM is developed, in which the whole image is decomposed first into sub-image blocks for parallel operation. Experimental results indicate that the ARVM method outperforms the RVM method and, in particular, the fast processing method could greatly reduce the testing time.
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Keywords:
- mammogram /
- microcalcification cluster /
- relevance vector machine /
- adaptive kernel learning
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[7] Xiao X, Xu L, Liu B Y 2013 Acta Phys. Sin. 62 044105 (in Chinese) [肖夏, 徐立, 刘冰雨 2013 62 044105]
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[17] Muller K R, Mika S, Ratsch G, Tsuda K, Scholkopf B 2001 IEEE Trans. Neural Networks 12 181
[18] Bunch P C, Hamilton J F, Sanderson G K, Simmons A H 1978 J. Appl. Photogr. Eng. 4 166
[19] Samuelson F W, Petrick N 2006 Proceedings of 3rd IEEE International Symposium On Biodedical Imaging Arlington, USA, April 4-6, 2006 p1312
[20] Xing H Y, Qi Z D, Xu W 2012 Acta Phys. Sin. 61 240504 (in Chinese) [行鸿彦, 祁峥东, 徐伟 2012 61 240504]
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[1] Ahmed M H, Magda E 2011 IEEE Reviws in Biomedical Engineering 4 103
[2] Zhang X S, Gao X B, Wang Y, Zhang S J 2010 J. Infrared Millim Waves 29 27 (in Chinese) [张新生, 高新波, 王颖, 张士杰 2010 红外与毫米波学报 29 27]
[3] Liu G D, Zhang Y R 2011 Acta Phys. Sin. 60 074303 (in Chinese) [刘广东, 张业荣 2011 60 074303]
[4] Xiang L Z, Xing D, Guo H, Yang S H 2009 Acta Phys. Sin. 58 4610 (in Chinese) [向良忠, 邢达, 郭华, 杨思华 2009 58 4610]
[5] Zhang H 2004 Acta Phys. Sin. 53 2515 (in Chinese) [张航 2004 53 2515]
[6] Xu X H, Li H 2008 Acta Phys. Sin. 57 4623 (in Chinese) [徐晓辉, 李晖 2008 57 4623]
[7] Xiao X, Xu L, Liu B Y 2013 Acta Phys. Sin. 62 044105 (in Chinese) [肖夏, 徐立, 刘冰雨 2013 62 044105]
[8] Che L L, Zhang G Y, Song L, Cao W F 2011 Chin. J. Med. Phys. 28 2467 (in Chinese) [车琳琳, 张光玉, 宋莉, 曹卫芳 2011中国医学物理学杂志 28 2467]
[9] Tang J, Rangayyan R M, Xu J, Naqa I El, Yang Y Y 2009 IEEE Trans. Inform. Technol. Biomed. 13 236
[10] Jing H, Yang Y Y, Nishikawa R M 2011 Phys. Med. Biol. 56 1
[11] Jiang J, Yao B, Wason A M 2007 Comput. Med. Imag. Graph. 31 49
[12] Naqa I E, Yang Y Y, Wernick M N, Galatsanos N P, Nishikawa R M 2002 IEEE Trans. Med. Imag. 21 1552
[13] Wei L, Yang Y Y, Nishikawa R M, Wernick M N, Edwards A 2005 IEEE Trans. Med. Imag. 24 1278
[14] Tzikas D G, Likas A C, Galatsanos N P 2009 IEEE Trans. Neural Networks 20 926
[15] Tipping M, Faul A 2003 Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics Key West, USA, January 3-6, 2003 p1
[16] Kallergi M, Carney G M, Gaviria J 1999 Med. Phys. 26 267
[17] Muller K R, Mika S, Ratsch G, Tsuda K, Scholkopf B 2001 IEEE Trans. Neural Networks 12 181
[18] Bunch P C, Hamilton J F, Sanderson G K, Simmons A H 1978 J. Appl. Photogr. Eng. 4 166
[19] Samuelson F W, Petrick N 2006 Proceedings of 3rd IEEE International Symposium On Biodedical Imaging Arlington, USA, April 4-6, 2006 p1312
[20] Xing H Y, Qi Z D, Xu W 2012 Acta Phys. Sin. 61 240504 (in Chinese) [行鸿彦, 祁峥东, 徐伟 2012 61 240504]
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