We describe a decision support system to distinguish among hematology cases directly from microscopic specimens. The system uses an image database containing digitized specimens from normal and four different hematologic malignancies. Initially, the nuclei and cytoplasmic components of the specimens are segmented using a robust color gradient vector flow active contour model. Using a few cell images from each class, the basic texture elements (textons) for the nuclei and cytoplasm are learned, and the cells are represented through texton histograms. We propose to use support vector machines on the texton histogram based cell representation and achieve major improvement over the commonly used classification methods in texture research. Experiments with 3,691 cell images from 105 patients which originated from four different hospitals indicate more than 84% classification performance for individual cells and 89% for case based classification for the five class problem.

中文翻译：

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使用文本特征对血液系统恶性肿瘤进行分类。

我们描述了一个决策支持系统，可以直接从显微镜标本中区分血液学病例。该系统使用包含来自正常和四种不同血液系统恶性肿瘤的数字化标本的图像数据库。最初，样本的细胞核和细胞质成分使用稳健的颜色梯度矢量流活动轮廓模型进行分割。使用来自每个类的一些细胞图像，学习细胞核和细胞质的基本纹理元素（textons），并通过 texton 直方图表示细胞。我们建议在基于文本直方图的单元表示上使用支持向量机，并对纹理研究中常用的分类方法进行重大改进。实验3，