Hu Erzhong, Nosato Hirokazu, Sakanashi Hidenori, Murakawa Masahiro
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5477-80. doi: 10.1109/EMBC.2013.6610789.
Capsule endoscopy is a patient-friendly endoscopy broadly utilized in gastrointestinal examination. However, the efficacy of diagnosis is restricted by the large quantity of images. This paper presents a modified anomaly detection method, by which both known and unknown anomalies in capsule endoscopy images of small intestine are expected to be detected. To achieve this goal, this paper introduces feature extraction using a non-linear color conversion and Higher-order Local Auto Correlation (HLAC) Features, and makes use of image partition and subspace method for anomaly detection. Experiments are implemented among several major anomalies with combinations of proposed techniques. As the result, the proposed method achieved 91.7% and 100% detection accuracy for swelling and bleeding respectively, so that the effectiveness of proposed method is demonstrated.
胶囊内镜是一种受患者欢迎的内镜检查方法，广泛应用于胃肠道检查。然而，诊断效果受到大量图像的限制。本文提出了一种改进的异常检测方法，有望检测小肠胶囊内镜图像中的已知和未知异常。为实现这一目标，本文引入了使用非线性颜色转换和高阶局部自相关（HLAC）特征的特征提取，并利用图像分割和子空间方法进行异常检测。在所提出技术的组合下，针对几种主要异常进行了实验。结果表明，所提出的方法对肿胀和出血的检测准确率分别达到了91.7%和100%，从而证明了该方法的有效性。
