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Segmenting Reddish Lesions in Capsule Endoscopy Images Using a Gastrointestinal Color Space

机译:使用胃肠道色彩空间分割胶囊内窥镜图像中的红色病变

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Segmenting reddish lesions in capsule endoscopy (CE) images is an initial step for further computer-assisted applications such as image enhancement, abnormal measurement/tracking, and so on. In this paper, we propose an automatic segmentation method that is successful even with CE image including unclear reddish lesions. To obtain this, the proposed method seeks good features to discriminate the reddish lesions from normal tissues. For implementations, we first extract only meaningful regions in a CE image through a pre-segmentation step. The proposed features then are extracted for the meaningful regions in stead of the whole image. We approaches segmentation task through considering a statistical operator for the extracted features, that is local mean image. Candidates of the abnormal regions are located in the local mean image with assistants of a diffusion process. Evaluations in the experiments confirm effectiveness of the proposed method with both qualitative and quantitative measurement.
机译:在胶囊内窥镜(CE)图像中分割微红的病变是进一步计算机辅助应用(如图像增强,异常测量/跟踪等)的第一步。在本文中,我们提出了一种自动分割方法,该方法即使在CE图像中包括不清晰的微红色病变时也能成功。为此,所提出的方法寻求良好的特征以将红色的病变与正常组织区分开。对于实现,我们首先通过预分段步骤仅在CE图像中提取有意义的区域。然后针对有意义的区域而不是整个图像提取提出的特征。我们通过为提取的特征(即局部均值图像)考虑统计算子来实现分割任务。异常区域的候选者在扩散过程的辅助下位于局部均值图像中。实验中的评估通过定性和定量测量证实了该方法的有效性。

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