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Feature Extraction and Disease Stage Classification for Glioma Histopathology Images

机译：胶质瘤组织病理学图像的特征提取和疾病阶段分类

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This paper discusses the performance of feature descriptors for disease stage evaluation of Glioma images. In the field of histopathology, many evaluation methods for tissue images have been reported. However, pathologists have to analyze and evaluate many tissue images manually. In addition, the criteria of evaluation heavily depend on each pathologist's experience and feelings. From this background, studies on computational pathology using computer vision have been reported. The proposed feature descriptors were, however, applied to specified diseases only, and we do not know whether these descriptors will be effective to other tissues or not. This paper applied the feature descriptors defined by previous studies to the Glioma images and investigated the effectiveness of them by using a statistical method. We also discussed a method to distinguish low-grade from high-grade Glioma images by using the significant descriptors. After the experiments, more than 98% of Glioma images were classified correctly.
机译：本文讨论了胶质瘤图像疾病阶段评估特征描述符的性能。在组织病理学领域中，已经报道了许多用于组织图像的评估方法。然而，病理学家必须手动分析和评估许多组织图像。此外，评估标准严重取决于每种病理学家的经验和感受。从该背景中，已经报道了使用计算机视觉的计算病理学研究。然而，所提出的特征描述符仅适用于特定疾病，我们不知道这些描述符是否会对其他组织有效。本文将通过先前研究定义的特征描述符应用于胶质瘤图像，并通过使用统计方法来研究它们的有效性。我们还讨论了一种通过使用重要描述符来区分低级胶质瘤图像的低级。实验结束后，将正确分类超过98％的胶质瘤图像。

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