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首页> 美国卫生研究院文献>Biomedical Engineering Letters >Computer-assisted brain tumor type discrimination using magnetic resonance imaging features
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Computer-assisted brain tumor type discrimination using magnetic resonance imaging features

机译：使用磁共振成像功能的计算机辅助脑肿瘤类型识别

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摘要

Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.
机译：医学成像在脑肿瘤的识别，分割和分类中起着不可或缺的作用。 MRI的发明为脑相关研究开辟了新的视野。最近，研究人员将重点转移到应用数字图像处理技术从MRI中提取，分析和分类脑肿瘤。脑肿瘤的分类是以从大到小的分级方式定义的。在有关脑肿瘤分类的文献中可以看到很多工作，如良性和恶性。但是，关于脑图像的多类分类的报道很少，其中包含肿瘤的图像的每个部分都用主要和次要类别标记。由于图像的模糊性和不同类型肿瘤的重叠特征，难以实现精确分类。在当前的研究中，提供了使用MRI对脑肿瘤多类别分类的最新研究的全面综述。这些多类分类研究分为两个主要组：XX和YY，每个组又分为三个子组。从经过审查的作品中提取一组通用参数，并进行比较，以突出各个作品的优缺点。根据我们的分析，我们为从事脑肿瘤分类领域的研究人员和专业人员提供了一系列建议。

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