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首页> 外文会议>International Conference on Communication and Electronics Systems >Robust cell detection of histopathological brain tumor images and analyzing its textual features
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Robust cell detection of histopathological brain tumor images and analyzing its textual features

机译：组织病理学脑肿瘤图像的稳健细胞检测并分析其文本特征

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Precise and accordant detection and prognosis, CAD (computer aided diagnosis) plays a fair role in predicting the outcome of the treatment and planning of the therapy. Detection and segmentation of the cells are the important steps in a CAD. These steps are difficult due to touching cells, untidy background and variation in the shapes of the cell and changes inside the nuclei. In this paper, we present an analysis based on the textual features of the detected cell after the detection of the cell using adaptive dictionary selection and the sparse reconstruction technique with trivial template. The analysis is done on the basis of the first order and second order statistical features. The proposed method has been tested on a data set with 1000 cells extracted from 20 whole slide scanned images.
机译：精确一致的检测和预后，CAD（计算机辅助诊断）在预测治疗结果和治疗计划中起着相当重要的作用。细胞的检测和分割是CAD中的重要步骤。由于接触细胞，不整齐的背景以及细胞形状的变化和细胞核内部的变化，这些步骤很困难。在本文中，我们基于自适应字典选择和具有小样模板的稀疏重建技术，基于检测到的单元格的文本特征进行了分析。分析是基于一阶和二阶统计特征进行的。在从20张完整的幻灯片扫描图像中提取的1000个细胞的数据集上对提出的方法进行了测试。

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