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

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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

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