Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization.
Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization.
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DOI：
10.4103/2153-3539.92031
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发表时间：
2011
影响因子：
--
通讯作者：
González FA
中科院分区：
文献类型：
--
作者：
Cruz-Roa A;Díaz G;Romero E;González FA
组织学图像是临床诊断和生物医学研究的重要资源。从图像理解的角度来看，这些图像的自动标注是一个具有挑战性的问题。本文提出了一种基于三种互补策略的病理组织图像自动标注新方法：第一种是基于部分的图像表示，称为特征袋，它利用病理组织图像的自然冗余来捕获生物结构的基本模式;第二种是基于非负矩阵分解的潜在主题模型，其捕获隐藏在图像中的高级视觉模式，以及第三，概率注释模型，其将形态学和结构特征的视觉外观与10个组织病理学图像注释相关联。该方法使用1,604个皮肤组织的注释图像进行评估，其中包括正常和病理的结构和形态特征，获得74%的召回率和50%的精确度，分别提高了64%和24%的基于支持向量机的基线注释方法。
Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively.