Anomaly detection for capsule endoscopy images using higher-order Local Auto Correlation features

Capsule endoscopy is a painless way more and more utilized in gastrointestinal examination. Nevertheless, there is an issue comes out that the efficiency and accuracy of capsule endoscopy diagnosis is now restricted by the large quantity of images. In this paper, an anomaly detection method for capsule endoscopy images captured within the range of small intestine is described. Aiming to realize the anomaly detection, this paper takes the advantage of Higher-order Local Auto Correlation features and subspace method using PCA (Principal Component Analysis). The proposed method is validated over capsule endoscopy image sets and its effectiveness is demonstrated by experimental results.