Xing Xiaohan, Jia Xiao, Meng Max-H Q
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1-4. doi: 10.1109/EMBC.2018.8513012.
Wireless Capsule Endoscopy (WCE) has become increasingly popular in clinical gastrointestinal (GI) disease diagnosis, benefiting from its painless and noninvasive examination. However, reviewing a large number of images is time-consuming for doctors, thus a computer-aided diagnosis (CAD) system is in high demand. In this paper, we present an automatic bleeding detection algorithm that consists of three stages. The first stage is the preprocessing, including key frame extraction and edge removal. In the second stage, we discriminate the bleeding frames using a novel superpixelcolor histogram (SPCH) feature based on the principle color spectrum, and then the decision is made by a subspace KNN classifier. Thirdly, we further segment the bleeding regions by extracting a 9-D color feature vector from the multiple color spaces at the superpixel level. Experimental results with an accuracy of 0.9922 illustrate that our proposed method outperforms the state-of-the-art methods in GI bleeding detection with low computational costs.
无线胶囊内镜（WCE）因其无痛、无创的检查方式，在临床胃肠（GI）疾病诊断中越来越受欢迎。然而，医生查看大量图像非常耗时，因此对计算机辅助诊断（CAD）系统的需求很高。在本文中，我们提出了一种自动出血检测算法，该算法包括三个阶段。第一阶段是预处理，包括关键帧提取和边缘去除。在第二阶段，我们基于主色谱原理，使用一种新颖的超像素颜色直方图（SPCH）特征来区分出血帧，然后由子空间KNN分类器进行决策。第三，我们通过在超像素级别从多个颜色空间中提取9维颜色特征向量，进一步分割出血区域。准确率为0.9922的实验结果表明，我们提出的方法在胃肠出血检测中以较低的计算成本优于现有方法。
