王帅,刘娟,毕姚姚,陈哲,郑群花,段慧芳.基于两步聚类和随机森林的乳腺腺管自动识别方法[J].计算机科学,2018,45(3):247-252
基于两步聚类和随机森林的乳腺腺管自动识别方法
Automatic Recognition of Breast Gland Based on Two-step Clustering and Random Forest
投稿时间:2017-06-22  修订日期:2017-08-22
DOI:10.11896/j.issn.1002-137X.2018.03.039
中文关键词:  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
英文关键词:Breast cancer,Histopathology image,Histopathology diagnosis,Gland segmentation,Gland recognition,Morphological operations,Computer-aided diagnosis
基金项目:
作者单位E-mail
王帅 武汉大学计算机学院 武汉430072  
刘娟 武汉大学计算机学院 武汉430072  
毕姚姚 武汉大学计算机学院 武汉430072 1311060843@qq.com 
陈哲 武汉大学计算机学院 武汉430072  
郑群花 武汉大学计算机学院 武汉430072 961290964@qq.com 
段慧芳 武汉大学计算机学院 武汉430072 774570667@qq.com 
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中文摘要:
      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。
英文摘要:
      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.
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