计算机科学 ›› 2018, Vol. 45 ›› Issue (3): 247-252.doi: 10.11896/j.issn.1002-137X.2018.03.039

• 图形图像与模式识别 • 上一篇    下一篇



  1. 武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072
  • 出版日期:2018-03-15 发布日期:2018-11-13

Automatic Recognition of Breast Gland Based on Two-step Clustering and Random Forest

WANG Shuai, LIU Juan, BI Yao-yao, CHEN Zhe, ZHENG Qun-hua and DUAN Hui-fang   

  • Online:2018-03-15 Published:2018-11-13

摘要: 腺管的自动识别在乳腺癌的组织病理学诊断中十分关键,因为腺管密度 是乳腺癌分级中的一个重要因子。腺管由一个周围充满细胞质的中心管腔以及管腔周围均匀环绕的细胞核组成。若管腔、细胞质、细胞核 在空间位置上接近,则意味着这可能是一个腺管,但是这种识别方法会因为乳腺组织切片中存在脂肪、气泡以及其他类似管腔的对象而出现假阳性错误。为了解决上述问题,提出基于二次聚类与随机森林的腺管自动识别方法。首先通过一次聚类和二次聚类构建出待分割图片;然后通过形态学操作对图片进行处理,并在此基础上进行分割,进而构建候选腺管,利用中心管腔与其周围细胞核的空间位置关系以及一些统计特征来描述腺管;最后通过随机森林分类算法进行分类。实验结果表明,所提算法可以达到86%以上的准确率,为乳腺癌的自动分级奠定了基础。

关键词: 乳腺癌,病理图像,病理诊断,腺管分割,腺管识别,形态学操作,计算机辅助诊断

Abstract: Automatic recognition of the glands is critical in the histopathology diagnosis of breast cancer,as glandular density is an important factor in breast cancer grading.The gland is composed of a central lumen filled with cytoplasm and a ring of nuclei around the lumen.The spatial proximity of the lumen,cytoplasm,and nucleus may mean that it is a gland,but this method can lead to false-positive errors due to the presence of fat,bubbles and other lumen-like objects in the breast tissue section.In order to solve the above problems,this paper presented an automatic recognition method of breast gland based on two-step clustering and random forest.First,the images to be segmented are constructed by clustering and two-step clustering.A series of morphological operations are performed on the images to repair the objects.Then the segmentation is performed.After that,the method builds the candidate glands,and utilizes the spatial position relationship between central lumen and the nucleus around the lumen and some other features to describe glands.By using random forest classification algorithm,the experimental results show that more than 86% accuracy can be achieved.The result lays the foundation for breast cancer automatic grading.

Key words: Breast cancer,Histopathology image,Histopathology diagnosis,Gland segmentation,Gland recognition,Morphological operations,Computer-aided diagnosis

[1] LATSON L,SEBEK B,POWELL K A.Automated cell nuclear segmentation in color images of hematoxylin and eosin-stained breast biopsy[J].Analytical and Quantitative Cytology and Histology/the International Academy of Cytology and American Society of Cytology,2003,25(6):321-331.
[2] PETUSHI S,GARCIA F U,HABER M M,et al.Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer[J].BMC Medical Imaging,2006,6(1):1-11.
[3] BAMFORD P,LOVELL B.Unsupervised cell nucleus segmentation with active contours[J].Signal Processing,1998,71(2):203-213.
[4] XU J,JANOWCZYK A,CHANDRAN S,et al.A weightedmean shift,normalized cuts initialized color gradient based geodesic active contour model:Applications to histopathology image segmentation[C]∥Medical Imaging:Image Processing.2010.
[5] SHI J,MALIK J.Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888-905.
[6] CHAN T F,VESE L A.Active contours without edges[J].IEEE Transactions on image processing,2001,10(2):266-277.
[7] BASAVANHALLY A,YU E,XU J,et al.Incorporating domain knowledge for tubule detection in breast histopathology using O’Callaghan neighborhoods[C]∥Proceedings of SPIE-The International Society for Optics and Photonics.2011.
[8] MAQLIN P,THAMBURAJ R,MAMMEN J J,et al.Automatic detection of tubules in breast histopathological images[M]∥Proceedings of Seventh International Conference on Bio-Inspired Computing:Theories and Applications (BIC-TA 2012).India:Springer,2013:311-321.
[9] NGUYEN K,BARNES M,SRINIVAS C,et al.Automatic glandular and tubule region segmentation in histological grading of breast cancer[C]∥SPIE Medical Imaging.International Society for Optics and Photonics.2015.
[10] SIRINUKUNWATTANA K,PLUIM J P W,CHEN H,et al.Gland segmentation in colon histology images:The glas challenge contest[J].Medical Image Analysis,2016,35:489-502.
[11] JANOWCZYK A,MADABHUSHI A.Deep learning for digital pathology image analysis:A comprehensive tutorial with selec-ted use cases[J].Journal of Pathology Informatics,2016,7(1):29.
[12] PENG Y,JIANG Y,EISENGART L,et al.Segmentation ofprostatic glands in histology images[C]∥2011 IEEE International Symposium on Biomedical Imaging:From Nano to Macro.IEEE,2011:2091-2094.
[13] NGUYEN K,SARKAR A,JAIN A K.Structure and context in prostatic gland segmentation and classification[M]∥Medical Image Computing and Computer-Assisted Intervention.Springer Berlin Heidelberg,2012:115-123.

No related articles found!
Full text



[1] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75, 88 .
[2] 夏庆勋,庄毅. 一种基于局部性原理的远程验证机制[J]. 计算机科学, 2018, 45(4): 148 -151, 162 .
[3] 厉柏伸,李领治,孙涌,朱艳琴. 基于伪梯度提升决策树的内网防御算法[J]. 计算机科学, 2018, 45(4): 157 -162 .
[4] 王欢,张云峰,张艳. 一种基于CFDs规则的修复序列快速判定方法[J]. 计算机科学, 2018, 45(3): 311 -316 .
[5] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[6] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[7] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[8] 刘琴. 计算机取证过程中基于约束的数据质量问题研究[J]. 计算机科学, 2018, 45(4): 169 -172 .
[9] 钟菲,杨斌. 基于主成分分析网络的车牌检测方法[J]. 计算机科学, 2018, 45(3): 268 -273 .
[10] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99, 116 .