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Local fractal dimension based approaches for colonic polyp classification

机译:基于局部分形维数的结肠息肉分类方法

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This work introduces texture analysis methods that are based on computing the local fractal dimension (LFD; or also called the local density function) and applies them for colonic polyp classification. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa) and on a zoom-endoscopic image database using narrow band imaging. In this paper, we present three novel extensions to a LFD based approach. These extensions additionally extract shape and/or gradient information of the image to enhance the discriminativity of the original approach. To compare the results of the LFD based approaches with the results of other approaches, five state of the art approaches for colonic polyp classification are applied to the employed databases. Experiments show that LFD based approaches are well suited for colonic polyp classification, especially the three proposed extensions. The three proposed extensions are the best performing methods or at least among the best performing methods for each of the employed databases.
机译:这项工作介绍了基于分析局部分形维数(LFD;也称为局部密度函数)的纹理分析方法,并将其应用于结肠息肉分类。该方法在8个高清内窥镜图像数据库上进行了测试,其中每个数据库都是使用不同的成像方式(Pentax的i-Scan技术结合或不对粘膜进行染色)获得的,并且在使用窄带成像的变焦内窥镜图像数据库上进行了测试。在本文中,我们对基于LFD的方法提出了三种新颖的扩展。这些扩展还提取图像的形状和/或梯度信息,以增强原始方法的判别能力。为了将基于LFD的方法的结果与其他方法的结果进行比较,将五个用于结肠息肉分类的最先进方法应用于所采用的数据库。实验表明,基于LFD的方法非常适合结肠息肉的分类,尤其是三个建议的扩展。对于每个所采用的数据库,三个建议的扩展是性能最好的方法,或者至少是性能最好的方法之一。

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