Computer Science ›› 2015, Vol. 42 ›› Issue (Z11): 195-198.

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Research on Segmentation Methods in Breast Computer-aided Detection

SHEN Kun-xiao, LAN Yi-hua, LU Yu-ling, SHANG Nai-li and MA Xiao-pu   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Breast cancer, as one of the common malignant tumor,remains a leading cause of cancer deaths among women.Early diagnosis and treatment is an efficient way of reducing the morbidity of breast cancer.Computer-aided diagnosis(CAD) can improve the efficiency and accuracy of diagnosis.A brief review of breast mass segmentation was provided in this thesis.We further analyzed and compared the advantages and performance of these methods.Finally,some means of the methods used to improve the segmentation accuracy were summarized.

Key words: Computer-aided diagnosis(CAD),Mammogram,CBIR,Breast mass,Image segmentation

[1] 虞红伟.基于活动轮廓模型的乳腺X线图像肿块分割方法的研究[D].杭州:杭州电子科技大学,2010
[2] 王靖.乳腺X图像肿块检测与分类方法研究 [D].北京:北京交通大学,2011
[3] 杨谊,申洪.自适应水平集方法乳腺超声肿块分割应用[J].计算机应用研究,2013,0(12):3840-3843
[4] Silverstein M,Recht A,Lagios M,et al.Image-detected breast cancer:state-of-the-art diagnosis and treatment[J].Journal of the American College of Surgeons,2009,209(4):504-520
[5] Siegel R,Nnishadham D,JEMAL A.Cancer statistics,2012 [J].CA:a cancer Journal for Clinicians,2012,62(1):10-29
[6] Papadopoulos A,Fotiadis D I,Likas A.Characterization of Clustered Microcalcifications in Digitized Mammograms Using Neural Networks and Support Vector Machine[J].Artificial Intelligence in Medicine,2005,34:141-150
[7] 舒松,周坤.钼靶X线摄影对乳腺癌的诊断价值[J].医学综述,2014,1(20):345-346
[8] Regentova E,Zhang L,Zheng J,et al.Microcalcification detection based on wavelet domain hidden Markov tree model:Study for inclusion to computer aided diagnostic prompting system.Medical Physics,2007,34(6):2206-2219
[9] 周悦.基于乳腺X线图像的计算机辅助诊断方法研究[D].苏州:苏州大学,2014
[10] Wang R,Wan B,Ma Z,et al.Computer-aided detection of microcalcifications in digital mammograms using a synthetic technique[C]∥International Society for Optics and Photonics Second International Conference on Image and Graphics.2002:639-644
[11] 万柏坤,王瑞平,朱欣,等.SVM算法及其在乳腺X片微钙化点自动检测中的应用[J].电子学报,2004,2(4):587-590
[12] 许向阳.乳腺钼靶图像中肿块检测方法研究[D].武汉:华中科技大学,2010
[13] 朱景升.基于乳腺X射线片的肿块检测方法研究 [D].武汉:华中科技大学,2012
[14] 方玲玲.图像分割的活动轮廓模型研究[D].苏州:苏州大学,2013
[15] 李妍.活动轮廓模型影像分割方法综述[J].遥感信息,2014,9(1):102-107
[16] 王沛,周鑫,彭荣鲲,等.结合边缘和区域的活动轮廓模型SAR图像目标轮廓提取[J].中国图像图形学报,2014,9(7):1095-1103
[17] 吕泽华,赵盛荣,梁虎,等.基于Gmac模型的乳腺肿块分割算法[J].电子学报,2014,42(2):398-404
[18] Timp S,Karssemeijer N.A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography[J].Medical Physics,2004,3(5):957-971
[19] 王小芳.基于活动轮廓模型的图像分割算法研究[D].长沙:中南大学,2011
[20] 严学强,叶秀清,刘济林,等.基于量化图像直方图的最大熵阈值处理算法[J].模式识别与人工智能,1998,1(3):352-358
[21] 俞勇,施鹏飞,赵立初.基于最小能量的图像分割方法[J].红外与激光工程,1998,8(4):20-27
[22] 程杰.一种基于直方图的分割方法[J].华中理工大学学报,1999,7(1):20-23
[23] 张建,汪天富,李德玉,等.基于对称区域生长算法的超声医学图像分割方法[J].生物医学工程学杂志,2007,4(3):500-503
[24] 王广君,田金文,柳健.基于四叉树结构的图像分割技术[J].华中科技大学学报,2001,0(1):12-14
[25] 屈彬,王景熙.一种基于区域生长规则的快速边缘跟踪算法[J].四川大学学报,2002,4(2):100-103
[26] 杨斌,宋立新.基于自适应区域生长的乳腺肿块分割方法[J].计算机工程与应用,2014(20):171-175,0
[27] 张深毅.基于参考图像的乳腺肿块诊断方法研究[D].武汉:华中科技大学,2011
[28] 姜娈.基于乳腺X线摄片的计算机辅助检测肿块方法研究[D].武汉:华中科技大学,2009
[29] 兰义华.基于图像内容检索的乳腺肿块诊断方法研究 [D].武汉:华中科技大学,2011
[30] 陈桂林,汪家旺.改良区域生长算法自动分割乳腺肿块图像的诊断价值[J].江苏医药,2011,37(13):1551-1553
[31] Dominguez A R,Nandi A K.Improved Dynamic Programming-based Algorithms for Segmentation of Masses in Mammograms[J].Medical Physics,2007,4(11):4256-4269
[32] 乔剑敏.基于GAC模型和C-V模型的图像分割方法的改进[D].哈尔滨:哈尔滨工业大学,2011

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