Iakovidis Dimitris K, Chatzis Dimitris, Chrysanthopoulos Panos, Koulaouzidis Anastasios
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:731-4. doi: 10.1109/EMBC.2015.7318466.
Wireless capsule endoscopy (WCE) enables screening of the gastrointestinal (GI) tract with a miniature, optical endoscope packed within a small swallowable capsule, wirelessly transmitting color images. In this paper we propose a novel method for automatic blood detection in contemporary WCE images. Blood is an alarming indication for the presence of pathologies requiring further treatment. The proposed method is based on a new definition of superpixel saliency. The saliency of superpixels is assessed upon their color, enabling the identification of image regions that are likely to contain blood. The blood patterns are recognized by their color features using a supervised learning machine. Experiments performed on a public dataset using automatically selected first-order statistical features from various color components indicate that the proposed method outperforms state-of-the-art methods.
无线胶囊内窥镜检查（WCE）能够通过一个封装在可吞咽小胶囊内的微型光学内窥镜对胃肠道（GI）进行筛查，并无线传输彩色图像。在本文中，我们提出了一种在当代WCE图像中自动检测血液的新方法。血液是需要进一步治疗的病变存在的警示迹象。所提出的方法基于超像素显著性的新定义。通过超像素的颜色来评估其显著性，从而能够识别可能包含血液的图像区域。使用监督学习机器通过血液图案的颜色特征来识别它们。在一个公共数据集上进行的实验，使用从各种颜色分量中自动选择的一阶统计特征，结果表明所提出的方法优于现有方法。
