Computer Science ›› 2018, Vol. 45 ›› Issue (3): 247-252.doi: 10.11896/j.issn.1002-137X.2018.03.039

Previous Articles     Next Articles

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

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] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .